HUMAN POSTURE DETECTION WITH THE HELP OF LINEAR SVM AND HOG FEATURE ON GPU

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1 International Journal of Computer Engineering and Applications, Volume IX, Issue VII, July 2015 HUMAN POSTURE DETECTION WITH THE HELP OF LINEAR SVM AND HOG FEATURE ON GPU Vaibhav P. Janbandhu 1, Sanjay B. Thakare 2 1 Department of Computer Engineering, ME student, JSPM's RSCOE, Pune University, India 2 Department of Computer Engineering, Asst. Prof., JSPM's RSCOE, Pune University, India. ABSTRACT: Detection of human posture is a research topic in visual research which finds its application in many areas such as intelligent digital content manager, social robotics and human activity reorganization, crowd behavior analysis and control, etc. Apart from this human posture detection is also used in pedestrian detection, 2D-3D animation, surveillance and robotics. Using the human posture detection method in real system is a challenging problem. As it require to compute the detection process in real time. Parallel computation provides the functionality to run the computation in minimum time utilizing all the resources. This paper provides an approach to run the human detection system with parallel feature extraction method in GPU. Support vector machine is a suitable technique for high dimensional feature space such as an image. HOG and BO features are combined together to form a single feature called fusion feature vector with linear SVM and better results are observed. To reduce the time the support vector machine is used by running the HOG feature in parallel with the help of GPU. By calculating features in parallel the time required for computation is reduced. The method is capable of detecting the human posture with SVM and fusion feature vector for INRIA dataset. Keywords: Block Orientation (BO) feature, Classification, Histogram of gradient (HOG), Support vector machine (SVM). [1] INTRODUCTION Human posture detection is the popular research topic in vision area after face detection as it is very complex task and consists of different variation of views and posture. Human posture detection is the process of detecting the human posture in an image as full body posture or part of the human posture. Detecting human posture is the popular theme in vision study and it has large numbers of applications. Human detection finds its applications in 2D-3D motion capture, driving assistance for pedestrian detection system, surveillance system, etc. Smart video surveillance system is used for surveillance of areas and required no human interaction. Such system requires analyzing the real time data and alerts the administrator to prevent any cataclysmic situation. Human posture detection can be used in such situation. In recent years road accidents are increased and safety of pedestrians on road has gained importance. Detecting the sudden appearance of pedestrians with the help of camera deployed on vehicles can warn the driver and increase the safety of footers on the road. In the 85

2 Human Posture Detection With The Help Of Linear SVM And HOG Feature On GPU world of digital cameras the amount of photos has increased exponentially and searching such a large amount of data for human posture can be done by human detection system. The human detection process becomes challenging as the variation of human pose and color increased. Illumination, color, scale, poses, etc. are the main reasons that make the human posture detection process tricky and difficult. To detect the human posture the main difficulty is to select the feature extraction method and a classifier. Support vector machine is the popular technique used to detect human posture. Many works on human posture detection with the help of support vector machine is put forwarded and good result is obtained on such system. Support vector machine is a machine learning technique that is used for classifying different number of classes. It is the best known and fastest approach for classification of two different classes. Support vector machine is the large margin classifier that classifies the different classes with maximum distance between two classes. There are two types of classification i.e. linear and non-linear classification. In linear classification the decision boundary is straight line and linear SVM is used for classifying only linearly separable data. If the data is not linearly separable then non-linear classification boundary is used to separate the two classes. Linear SVM can be used when the data is small in size and it is known that the data is linearly separable. When the dataset is large in size and it is not known whether the data is linearly separable or not in such cases non-linear support vector machine can be used to form a non-linear classification boundary. Soft margin SVM allows the error in the classification and can classify the data which are not separable. As support vector machine used only supports vector present at the boundary to draw the classification line, it requires only evaluating those support vectors that makes the SVM to work fast. Linear SVM and PLSVM i.e. piecewise linear SVM is used for human posture detection system. PLSVM uses more than one liner SVM for training and testing. Comparison of linear SVM and piecewise linear SVM is made with the fusion feature vector to test the accuracy. For convenience linear SVM and PLSVM is referred as SVM and more detail about PLSVM can be found in [1]. Figure 1: Showing human detection system output in rectangular boxes Parallel computation [2] [3] provides the functionality to utilize the physical resources of the CGPU and reduce the computation time by running the computation in parallel. Parallel computation use two or more cores or processor to solve the single problem. In serial computation the problem is run by single processor containing single core. A problem can be divided into small pieces and each piece can be run on different processor or core. Running the 86

3 International Journal of Computer Engineering and Applications, Volume IX, Issue VII, July 2015 problem on different core or processor is much better approach then running the problem in single core. Multi-core processors are available on which we can utilize and run each piece on each core separately so than parallelism can be achieved. The main objective of the paper is to detect human posture in images. Detection of human posture in common views and complex background has been put forward in recent years but it is a challenging problem in the situations of complex backgrounds and in large variations of views and postures, illumination, color and scale. Due to these problems it becomes hard detect human posture. To run the system in real time a method is required which increases the recognition rate and decrease the time of computation. The system must be capable to detect the human posture and increases the recognition rate and decrease the time of computation. HOG is mostly used with SVM to detect the human posture. The human posture detection system must be capable of detecting the human posture as shown in figure 1. This paper is mainly focused on improving the accuracy of SVM for human posture detection with fusion feature vector and extracting the HOG feature on GPU. The rest of the paper is arranged as follows; section 2 deals with the motivation for human posture detection. Section 3 shows the literature survey for the method used. Section 4 deal with the details of the human posture detection system. Section 5 covers details of graphical processing unit. Section 6 shows the results and section 7 concludes the paper. [2] MOTIVATION Human posture detection is a useful tool for higher-level applications that rely on visual input such as images and videos. For intelligent systems such as human-computer interaction and robotics understanding human activities are the solution to the many problems. The human posture detection system can be used for intelligent digital content manager in which images that contain human posture are automatically separated and tag for the image is generated. It can be used in social robotics and human interaction system where social behavior of the human is studied. For example autonomous mobile robots in the workplace or home could interact more seamlessly with the humans in their environment, if they could reliably detect their presence. It can be used in automated surveillance, such as intelligent software could monitor security cameras and detect suspicious behavior. Furthermore, human operators could search archived video for classes of activity that they specify without requiring manual viewing of each sequence. Having automated surveillance vastly increases the productivity of the human operator and increases coverage of the surveillance. It is used in safety devices for pedestrian detection on motor vehicles for example intelligent software on a camera-equipped car could detect pedestrians and warn the driver. Producing computer-generated imagery of realistic motion currently requires the use of a motion-capture system that stores the exact 2-D or 3-D motion of a human body using visual or radio markers attached to each limb of an actor. With accurate algorithms for human motion tracking, the same data could be acquired from any video without any additional equipment. Crowed behavior and analysis is another major area in which human detection system can be applied to study and analyze the crowed behavior. 87

4 Human Posture Detection With The Help Of Linear SVM And HOG Feature On GPU [3] LITERATURE SURVEY Support vector machine is the better and simple technique compared then non linear SVM. Some techniques and method for which SVM and other mechanism used as a classifier is mention here. Features for human posture detection are also discussed. Detection style algorithm [4] is used for the pedestrian detection by combining the appearances and motion information. It is very fast approach presented for detecting pedestrians. In [5] HOG (Histogram of oriented gradient) feature is used with linear SVM and an excellent result for human detection is observed which reduces the false positive rate in detection process. HOG descriptor performance better when compared to other existing feature sets such as wavelets. Cascade-of-rejecters with HOG [6] is the method for feature extraction that appreciably speedup the detection process. In [7] Gabor filter is used with linear SVM, the system performs better but limitation in processing speed is observed. Tree structured classifier [8] models is used to detect multiple views and postures of object. This method follows the divide and conquers approach and can be used with any of the feature. Divide and conquer method also shows better detection accuracy in which classifier such as tree structure and pyramid boosting classifier is used. But such methods are prone to error. Non liner kernel SVM s are proposed in [9] and it is observed that the proposed system performs better than the linear SVM but the computational cost is very high for kernel SVM. In [10] the author derived excellent detection accuracy on INRIA dataset, for this purpose HOG is combined with Local Binary Pattern. In [11] the author proposed a multi-classification SVM model that can be used with both linear and non-linear SVM. The multi-classification SVM model performs fairly compared to other methods. Deformable part-based model [12] is used in which the human detection is done by segmenting the human body indifferent parts. In experiments it is observed that the system results contain false positives. Localized SVM and Profile SVM are used in [13] and the proposed method outperforms non-linear SVM. Efficient sub-window cascade [14] is the method for classifier evaluation technique that appreciably speedup the detection process. In [15] the author derived a system with less false positive results. Linear SVM performs better in computational time but in detection accuracy the system achieves lower results than non-linear SVM. In [16] non liner kernel SVM s are used and the system is affected by noise and dimensionality but the performance is better than the traditional approaches. In [1] piecewise linear SVM is proposed by author for human posture detection. In this method BO i.e. Block Orientation feature is introduces which act as a background substitution for human posture detection. More detail study of the other methods is found in [17] which is review paper SVM and HOG. In [18] parallel HOG is implemented for tracking purpose but this method is different as compared to the method presented in this paper. In [19] histogram of orientation is run in parallel with the help of GPUs and better result in terms of time s observed when compared to CPU. 88

5 International Journal of Computer Engineering and Applications, Volume IX, Issue VII, July 2015 [4] HUMAN POSTURE DETECTION SYSTEM There are mainly two features used for human posture detection, they are HOG and BO. In feature extraction the HOG features are extracted based on the gradient and the orientation. The HOG feature is extracted by dividing the image into 64x128 pixels. Each 8x8 pixel are combined to form a one cell and a 2x2 cell is combined to form one block as shown in figure 2. From each cell a 9-dimentional feature are extracted. Figure 2: Division of image in window size, block and cell To calculate BO feature a 420 cell are divided into left-right and up-down sub-cells and BO features [1] are extracted. The BO feature act as a background substitution for human posture detection system. The horizontal and vertical gradient is shown as: { } { } (1) Here I(x) represent the R, G, B color value and BO feature is represented in normalization form. Here to reduce noise effect is used. The BO feature is calculated as follows: (2) In [1] HOG and BO feature are used in serial manner i.e. HOG followed by BO. A new feature vector is developed by combining HOG and BO feature and named as fusion feature vector. Instead of using HOG and BO feature separately or in serial manner they are combined to form a single feature vector. To decrease the computation time the HOG feature extraction are computed in parallel with the help of GPU and the complete system is shown in figure 3. The HOG feature extraction method runs in parallel with the help of GPU. For each window size the blocks are calculated and each block is divided into cells. Let H= {H 1, H 2,, H k } is the of histogram for each block. On each block HOG feature is extracted and each HOG feature extraction H i is run by separate threads for each block. The block orientation feature is 89

6 Human Posture Detection With The Help Of Linear SVM And HOG Feature On GPU also run in parallel to HOG feature extraction. The GPU architecture helps to run the HOG method in parallel and reduces the time required to run compared to time required for CPU. Figure 3: Human detection system The training data is represented as. Where x n is the input vector and y n is the target class. The target class can be represented as, where -1 represent one class and 1 represents another class. where R n represents the coordinate space. The value of training instance is given by (3) Where b is the scalar and w is the vector in R n and it means that the weight vector w has k components and each function assign the weight to each of the k base function. Class 1 can be represented by and class 2 can be represented as. The decision boundary can be represented as. The distance of a point x n from can be calculated as (4) The linear SVM can be formulated as The SVM represented above is the type of hard margin where violation of the margin is not allowed. When the violation of the margin is allowed then it is the soft margin problem. Soft margin is used when the classes are overlapped or no clue about the dataset is known. Then the SVM can be represented as: (5) (6) 90

7 International Journal of Computer Engineering and Applications, Volume IX, Issue VII, July 2015 Where C is the constant that gives the relative importance to ξ and w and ξ represents the slack factor. Let H = {h 1, h 2,..,h n } and B= {B 1, B 2,, B n } be the set of histogram features for each block, that are calculated in parallel, and Block orientation feature for each fixed cells number. For a given training set X = {(x n, y n )}, n = 1,..., N, the SVM model can be represented as Here λ represents the parameter for training error and the SVM margins. ξ represents the slack factor. There are two main modules of SVM, they are training and testing and shown in figure 3. In training the SVM is trained to classify the human posture in an image. From training dataset features are extracted by HOG and BO feature extraction method and combined together to form fusion feature vector from each image. Iterations are performed on the datasets and SVM model classifier is developed. In testing, the test image is divided into windows, HOG on GPU and BO features are extracted and combined together to form fusion feature vector. The SVM model classifier is used to classify and detect the human posture in the image. If the image consists of human posture then it will be detected otherwise no human posture will be detected. [4.1] FUSION FEATURE VECTOR ALGORITHM (7) Input: Output: Step 1: Step 2: Step 3: Step 4: Step 5: Step 6: Step 7: Step 8: Image Dataset Output SVM model for human detection Global read the image size. Global read the window size. Divide the window in 2x2 cells and each cell into 8x8 pixels. H={H 1, H 2,, H k }is the histogram for each block. B={B 1, B 2,, B n } is the block orientation for image. Calculate the HOG feature extraction method in GPU by creating k thread for each cell to compute histogram. Calculate the BO feature extraction method with the help of CPU. Combine the HOG and BO feature to form fusion feature vector. Global write all histogram and Block orientation associated with window size and image size. Feed the feature for training and testing to the SVM classifier. Apart from INRIA [20] person dataset SDL dataset [21] and TUD-Brussels [22] can also be used. If there are F dimensional feature then the complexity of linear SVM is O(F). Parallel HOG is used to calculate the feature so the time required for feature extraction is less when compared to CPU. 91

8 Human Posture Detection With The Help Of Linear SVM And HOG Feature On GPU [5] HOG FEATURE ON GPU GPUs are generally used for graphical processing rendering and many 3D graphical processing. These GPUs have many cores associate with it, generally more than hundred. GPUs support numbers of thread which runs in parallel and allow non uniform access to memory. As GPUs contain many cores which can be utilize for non graphical processing purpose. Such computation is known as GPGPU i.e. General Purpose computation on Graphical Processing Unit. NVIDIA CUDA is the interface which is provided for executing the C program on the GPUs. Thus it provides direct access to the GPU without using a graphical API. By using CUDA multiple threads and multiple GPUs C functions can be implemented on GPUs. All these functions are known as kernels and a thread is executed on the single kernel at a given time. Threads can access different types of memory such as local, shared, register, global, constant and texture memory. The threads in the same block can access shared memory. All threads in the GPUs have access to the constant, texture and global memory. Every multiprocessor have their own shared memory and registers. Constant memory and texture memory are read only while all other memories are write and read. Register memory or shared memory access are much faster than global memory access. Access to constant memory and texture memory is fast as they are cached. The thread memory access pattern can increased the speed of the memory access. If threads of the same multiprocessor access the consecutive memory location than speed can be increased as fewer memory operation is required. Figure 4: Steps of HOG on GPU and CPU When working with the GPU one has to deal with the memory copy operation which is certainty a time consuming process. To run the program on GPU the content of the memory is to be transfer from CPU memory to GPU memory. As shown in the Figure 4, first the image is transferred from CPU main memory to the GPU main memory. After initializing the image 92

9 International Journal of Computer Engineering and Applications, Volume IX, Issue VII, July 2015 HOG descriptor is computed on GPU processor. After that the results of the HOG descriptor is transfer back to the CPU processor and HOG and BO features are combined to run on the SVM classifier. [6] EXPERIMENTAL RESULTS For human posture detection INRIA Person dataset [20] is used which is the most popular dataset for pedestrian detection. It consists of training and testing images. The images mainly consist of person standing, sporting and bicycling. The training dataset consist of 614 upright standing human posture positive images and negative images of 1218 non human posture i.e images in total. Figure 5: Some positive test images from INRIA Figure 6: Some negative test images from INRIA The test set consists of 741 images i.e. 288 positive images and 453 negative images. Figure 5 shows some of the positive dataset images and figure 6 shows some of the negative dataset images. By running SVM as a classifier and fusion feature as combination of HOG and BO on INRIA dataset high performance is observed. Figure 7 shows some of the output of human posture detection system. Comparison of linear SVM and piecewise linear SVM is provided in table 1. In piecewise linear SVM the HOG feature is followed by BO. Table 1 shows the recognition rate for piecewise linear SVM and linear SVM with and without fusion feature vector. Recognition rate for linear SVM with HOG is and for BO is But when fusion feature vector is used the recognition rate for linear SVM is increased to When piecewise linear SVM is used the recognition rate is , the recognition rate is increased to with the help of fusion feature vector. So fusion feature vector performs better when compared to HOG and BO separately, and HOG followed by BO. 93

10 Human Posture Detection With The Help Of Linear SVM And HOG Feature On GPU Figure 7: Output of human detection system for INRIA dataset. Table 1: comparison of linear SVM and piecewise linear SVM Task Feature Recognition Rate Linear SVM PLSVM HOG BO Fusion feature HOG / BO Fusion Feature To find the better recognition rate for piecewise linear SVM experiments are performed with increasing number of SVM. Table 2 shows the recognition rate of piecewise linear SVM for numbers of SVM used with and without fusion feature vector, figure 8 shows the graph for the same. Experiments shows that the piecewise SVM performs better when the number of SVM is greater or equal to 5, however when the SVM number is equal to 10 the recognition rate decreases. When HOG feature followed by BO feature is used the lowest recognition is for 1 number of SVM and highest recognition is for 9 number of SVM. When fusion feature vector is used the lowest recognition is for 1 number of SVM and highest recognition is for 5 to 9 number of SVM. It is also find out that fusion feature vector performs better in all cases. Figure 8: Graph for piecewise linear SVM with and without fusion feature vector. Table 3 shows the time comparison between CPU and GPU. The program is run on GPU which supports 768 cores, with Intel i5 processor. For training CPU takes sec and 94

11 International Journal of Computer Engineering and Applications, Volume IX, Issue VII, July 2015 GPU takes sec, which is 55% decrease in time. For testing the CPU takes sec and GPU takes sec, which is 35% decrease in time. So by running the HOG on GPU the time is reduced to 55% for training and 35% for testing. For processing a sample image for HOG feature extraction the time required for CPU is 1030 m sec and for GPU it takes 78 m sec. Figure 9 shows the time taken for extraction of HOG feature vector. For small number of images CPU performs better then GPU in terms of time. However as the number of images increases the time required for GPU decreases compared to CPU. The GPUs are better when the dataset used for human posture detection is large in number. As INRIA dataset contain 1832 training images and 741 testing images, the GPU is used to reduce the number of time. So there is near about 6% decrease in time for GPU as compared with CPU when there are large numbers of images. Table 2: recognition rate for piecewise linear SVM with and without fusion feature vector. No. of SVM Fusion Feature Vector Recognition Rate 1 Yes No Yes No Yes No Yes No Yes No Yes No Yes No Yes No Yes No Yes No Table 3: Time comparison between CPU and GPU Tasks CPU Time GPU Time Time taken for training (sec) (sec) Time taken for testing (sec) (sec) Time of Execution of HOG Feature for a Sample Image 78 (m sec) 1030 (m sec) 95

12 Human Posture Detection With The Help Of Linear SVM And HOG Feature On GPU [7] CONCLUSION Figure 9: Time taken for extraction of feature vector. Detecting human posture is still a research problem and a difficult task due to different variations of views and posture. There are several methods which are used for human detection. SVM is one of the most popular classifier that is used for Human detection. As SVM is used the computation complexity is less. HOG and BO feature vector is combined together to form a single fusion feature vector. This fusion feature vector is used with SVM and tested on INRIA dataset. Testing results shows better accuracy on INRIA dataset for both linear SVM and piecewise linear SVM. The HOG feature is run in GPU with 768 core supporting system to reduce the time, and decrease in time over CPU is observed. The proposed system is better in both terms of accuracy and time. The proposed method is working by detecting human posture with the help of SVM and parallel HOG feature extraction for an image. The future work includes detecting the human posture with motion and tracking. 96

13 International Journal of Computer Engineering and Applications, Volume IX, Issue VII, July 2015 REFERENCES [1] Ye, Qixiang, et al. "Human detection in images via piecewise linear support vector machines." Image Processing, IEEE Transactions on 22.2 (2013): [2] Fox, Geoffrey C., Roy D. Williams, and Guiseppe C. Messina. Parallel computing works!. Morgan Kaufmann, [3] Golub, Gene H., and James M. Ortega. Scientific computing: an introduction with parallel computing. Elsevier, [4] Viola, M. Jones, and D. Snow, Detecting pedestrians using patterns of motion and appearance, Int. J. Comput. Vis., vol. 63, no. 2, pp , [5] N. Dalal and B. Triggs, Histograms of oriented gradients for human detection, in Proc. IEEE Int. Conf. Comput. Vis. Pattern Recognit., Jun. 2005, pp [6] Q. Zhu, S. Avidan, M. Yeh, and K. Cheng, Fast human detection using a cascade of histograms of oriented gradients, in Proc. IEEE Int. Conf. Comput. Vis. Pattern Recognit., Jul. 2006, pp [7] T. Serre, L. Wolf, S. Bileschi, M. Riesenhuber, and T. Poggio, Object recognition with cortex-like mechanisms, IEEE Trans. Pattern Anal. Mach. Intell., vol. 29, no. 3, pp , Mar [8] B. Wu and R. Nevatia, Cluster boosted tree classifier for multi-view, multi-pose object detection, in Proc. IEEE Int. Conf. Comput. Vis., Oct. 2007, pp [9] S. Maji, A. C. Berg, and J. Malik, Classification using intersection kernel support vector machines is efficient, in Proc. IEEE Int. Conf. Comput. Vis. Pattern Recognit., Jun. 2008, pp [10] X. Wang, T. X. Han, and S. Yan, An HOG-LBP human detector with partial occlusion handling, in Proc. IEEE Int. Conf. Comput. Vis., Oct. 2009, pp [11] O. Oladunni and G. Singhal, Piecewise multi-classification support vector machines, in Proc. Int. Joint Conf. Neural Netw., Jun. 2009, pp [12] P. F. Felzenszwalb, R. B. Girshick, D. McAllester, and D. Ramanan, Object detection with discriminatively trained part based models, IEEE Trans. Pattern Anal. Mach. Intell., vol. 32, no. 9, pp , Sep [13] H. B. Cheng, P.-N. Tan, and R. Jin, Efficient algorithm for localized support vector machine, IEEE Trans. Knowl. Data Eng., vol. 22, no. 4, pp , Apr [14] C. H. Lampert, An efficient divide-and-conquer cascade for nonlinear object detection, in Proc. IEEE Int. Conf. Comput. Vis. Pattern Recognit., Jun. 2010, pp [15] M. Enzweiler and D. M. Gavrila, Multilevel mixture-of-experts framework for pedestrian classification, IEEE Trans. Image Process., vol. 20, no. 10, pp , Oct [16] Y. Li, B. Liu, X. Yang, Y. Fu, and H. Li, Multiconlitron: A general piecewise linear classifier, IEEE Trans. Neural Netw., vol. 22, no. 2, pp , Feb [17] Janbandhu, Vaibhav. "Human Detection with Non Linear Classification Using Linear SVM." Volume 3 Issue 12 December 2014 [18] Hassan, Waqas, et al. "PHACT: Parallel HOG and Correlation Tracking." IS&T/SPIE Electronic Imaging. International Society for Optics and Photonics, [19] Kmieć, Marcin, and Andrzej Glowacz. "Object detection in security applications using dominant edge directions." Pattern Recognition Letters 52 (2015):

14 Human Posture Detection With The Help Of Linear SVM And HOG Feature On GPU [20] Available: [21] Available: [22] Available: 98

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