Object Tracking using Superpixel Confidence Map in Centroid Shifting Method

Transcription

1 Indian Journal of Science and Technology, Vol 9(35), DOI: /ijst/2016/v9i35/101783, September 2016 ISSN (Print) : ISSN (Online) : Object Tracking using Superpixel Confidence Map in Centroid Shifting Method Richard Evan Sutanto, Lenny and Suk Ho Lee * Dongseo University, 47 Jurye-ro, Sasang-gu, Busan 47011, Korea; richardwenz91@gmail.com, lenny.pribadi@gmail.com, petrasuk@gmail.com Abstract Objectives: To help security system works better, many countries especially developed countries installed surveillance security cameras. They used it to help find desired person whether they are criminal or not. Methods/Statistical Analysis: In order to do object tracking task, using colour-based tracking algorithm will give more stable result. By trying to get with different approach, the method that proposed is came from two algorithms. There are Super pixel tracking and centroid shifting based for tracking. Because both of algorithms give promising results in order to do tracking task, it is good to take each of advantages character from both algorithms. Findings: The proposed method is used Super pixel confidence map to get region of the object and determine between object and background. By using Super pixel confidence map, the tracker will be able to discriminate by measure the value. If the value is high, it is more likely to the object. And if the value is low, it is more like to the background. Before it used super pixel confidence map value, it will do a centroid shifting based to find target location by weighted the area with mean of the centroids comparing to each color bin of the target. The experiment will compare proposed method with other previous algorithms, original tracking based on centroid shifting and super pixel tracking using a same dataset. Improvements/Applications: This algorithm can be helpful for enhanced other application such as Object Recognition, Person Re-initialization, and some other applications in deep learning especially for object recognition. Keywords: Centroid Shifting, Color; Object; Super Pixel; Tracking 1. Introduction Motion tracking and object detection become a hot topic recently, because in most developed countries they have surveillance that worked as safety tools which records conditions public places. As a security camera, motion tracking and object detection could be a tool to find any criminals that the authorities wanted. Tracking method is still in development phase and there will be many future works for its application, especially in security. Many researchers try to develop tracking methods mixed with some other tools to produce better results in CCTV environment to track certain people in order to find wanted person. Several works has been done to increase the performance and accuracy in motion tracking. In one of the earliest work 1, Robert proposed a method to track object through an image using meanshift algorithm. He used it to track 2D blobs in the image. Another approach in object tracking using mean-shift algorithm were also proposed by another researchers with different approaches. In 2 applied color histogram to increase the accuracy in mean-shift, In 3 use adaptive bandwidth to find the candidate model and Encheol Choi outperformed the original mean-shift by using target and background area weighted. In 4 also proposed another method by using centroid shifting algorithm in motion tracking. In their research, they use a colourbased tracking algorithm that has a good stability based on the target s new representation. The target location is found by calculating the area of the centroid that connected into each colour bin of the target. The result of this tracking algorithm method is good enough to do the tracking task even with many obstacles conditions. Some researcher also proposed another approach. They * Author for correspondence

2 Object Tracking using Superpixel Confidence Map in Centroid Shifting Method showed that a model that can be adapted easily will have a strong character in achieve robust object tracking 5-8. In 9 and 10 also has proposed a method based on robust tracker to do motion tracking using super pixel algorithm, they use robust tracker based on a discriminative appearance model and super pixel. The tracking method is expressed by calculating confidence maps and finding the best location by maximum a following approximation. This tracking method provides the tracker to discriminate between target and background. In their result, it presented that their discriminative appearance model with super pixel is given good performance in order a tracker handle many obstacles. In this paper, we propose a new method which combines both method from in. We use a tracking method based on centroid shifting together with the use of these upper pixel confidence map to get better accuracy. In the proposed algorithm the centroid shifting algorithm takes only the color into account which lie in a region which have large positive values in the target confidence map computed by the super pixel algorithm. Therefore, the weighted centroid shift becomes different from that of the mere centroid shifting method. This makes the algorithm more stable when the colors of the target and the background are similar and therefore, the tracking result becomes more accurate because it used super pixel confidence map value. 1.1 Superpixel Tracking Many methods can be used to perform object detection. Superpixel has been one of the methods that give good results in order to do the task. This method can divide images to become numbers of superpixels with some information of the objects that can be used to do construction. The algorithm can be used to track an object that have a smooth motion with many obstacles in the scene, and fast movement. Superpixel tracking will be used to compute a target-background confidence map and get the value to recalculate the shift of the object 10. The confidence map value of each superpixel can be computed using this equation: (1) (2) Here, denotes the weight between the feature of the r-th super pixel in the t-th frame and the feature center of the cluster. The parameter shows the radius of the cluster in the feature space, and is a term for normalization. After, all the weights T have been calculated for all the pixels, the confidence value can be calculated by the following equation: (2) 1.2 Centroid Shifting Based Tracking Kernel-based tracking algorithm is being used because of its workable computation and encouraging results with complicated camera motions and unscripted target motion. It uses information from color histogram with spatial information which is provided by the kernel then, the drift of the object position will be computed by using mean shift procedure. But in certain situations, the mere mean shift based have some loss constancy such as, fail to track an object that moved further from its original position. First, we calculate the centroid of the colour bins (M u ). This can be done using this equation: (3) Here, N b denotes the number of the pixels and X i are the position vectors to do domain calculation. Where define Kronecker delta function, and is used to combine to the pixel x i with the index of its color bin histogram. For each colour bin, M u is calculated respectively with equation (3). Then, the area weighted mean of the centroids represents the target location. Then, we use M u to compute ŷ 0 that is the location of the current centroid by using equation (3), where the n in shows the current frame. (4) After getting the current location, we move the calculation into the next frame to calculate ŷ 1. ŷ 1 is the next position of the current centroids in the next frame. To compute ŷ 1, we use the same method with ŷ 0, the only different is the frame. is the centroid in the next frame. The computation of ŷ 1 is illustrated in equation (5) (5) Using the result from equation (4) and equation (5), we can calculate the shifting vector ŷ shift by taking 2 Vol 9 (35) September Indian Journal of Science and Technology

3 Richard Evan Sutanto, Lenny and Suk Ho Lee the difference of ŷ 1 and ŷ 0. The computation of ŷ shift is illustrated in equation (6) ŷ shift = (6) We use ŷ shift to shift the current centroid location by adding it with ŷ 0. The purpose is to locate the target position in the next frame. 1.3 Proposed Work We propose another approach by combine two algorithms (centroid shifting and super pixel). As we can see, the result of both centroid shifting method and super pixel is fast and had a good constancy to track in difficult environments, and by combining those two algorithm, we can get better accuracy in motion tracking. In Figure 1, there are illustrations that show how the proposed method works. At the beginning, we use the centroid shifting tracking method which takes the colors according to the search area into account in the initial target region and then we use Super pixel tracking method to compute confidence map and get the value of it. Then we add the super pixel confidence map value to our equation (1) and make a new equation as we can see in equation (7). Figure 2. Algorithm work flow. 1.4 Initialize the Search Region In this section, we create a rectangular shape region around the target. This region is our workspace which is used to compute the tracking algorithm in each frame. Figure 1. Illustration of how the proposed method works. (a) Object inside search region in first frame. (b) Super pixel confidence map value that is calculated using equation (1). (c) Centroid shifting with superpixel confidence map value to calculate with equation (6). (d) Get the position of next frame. (e) Object position in the next frame. (7) M u = In order to do the object tracking, some steps are required to follow according its flow. Figure 2 shows the proposed method workflow. 1.5 Calculate Super Pixel Confidence Map After we create the search region, we calculate super pixel confidence map (S c (X i )) using the equation (1). This value will be used to calculate the centroid function. Compute and ŷ 0 In this step, we compute the centroid of the color bins (M u ) by using the equation (6). Then, we use M u to calculate the original position of the current centroid (ŷ 0 ) with equation (3). Compute and ŷ 1 By using the equation (6), we calculate the centroid ( where X i are now the pixels in the next frame. Then, using equation (4), we get the centroid position in the next frame (ŷ 1 ). Vol 9 (35) September Indian Journal of Science and Technology 3

4 Object Tracking using Superpixel Confidence Map in Centroid Shifting Method Figure 3. Tracking results. Compute ŷ shift To obtain the next location in the new frame, we calculate the difference vector of the two centroid position (ŷ 0 and ŷ 1 ). This difference is calculated by simply subtracting ŷ 1 with ŷ 0 as illustrate in equation (5). 1.6 Replace the Centroid Position After we calculate the ŷ shift, we shift the original centroid position to the next frame. And then we replace ŷ 0 with ŷ 1. Step 5 to step 7 is repeated for each frame until all frame are calculated. 2. Conclusion In this section, we will show how our experiments work and its results. We used 2 different methods as comparison which are Super pixel Tracking and Motion Tracking using Centroid Shifting. The datasets obtained by a moving camera with pan/tilt options. As we can see in Figure 3, there are big differences between Superpixel Tracking and Motion Tracking using Centroid Shifting, while the differences between Motion Tracking and Proposed Method are smaller. As has been seen, the proposed method gives good results in order to do tracking task. It is improved accuracy of the previous methods which is centroid shifting and also super pixel tracking itself. According to the experiment result, we can see that by combining confidence map of Super pixel into centroid shifting algorithm, given a better performance compare with previous algorithms. There are some future works in this experiment, we will improve the accuracy even more than the proposed method given by using more sequence of dataset with more occultation. We will also develop better construction of its code to perform faster computation. Another future works that can be applied is in machine learning area, which object tracking become one of most popular and most recent under development area for machine vision Acknowledgement This work was supported by the Basic Science Research Program (NRF-2013R1A1A4A ) through the National Research Foundation of Korea (NRF) funded by the Ministry of Education, Science and Technology. 4. References 1. Collin RT. Mean-shift blob tracking through scale space. Computer Vision and Pattern Recognition, Proceedings IEEE Computer Society Conference on Jun, p Xu D, Wang Y, An J. Applying a new spatial color histogram in mean-shift based tracking algorithm. Proceeding of Image and Vision Computing Conference, New Zealand, Chen X, Zhou Y, Huang X, Li C. Adaptive Bandwidth Mean 4 Vol 9 (35) September Indian Journal of Science and Technology

5 Richard Evan Sutanto, Lenny and Suk Ho Lee Shift Object Tracking IEEE Conference on Robotics, Automation and Mechatronics Sep, p Lee SH, Kang MG. Motion Tracking based on area and level set weighted centroid shifting. IET Computer Vision Jun; 4(2): Santner J, Leistner C, Saffri A, Pock T, Bischof H. PROST: Parallel Robust Online Simple Tracking, Computer Vision and Pattern Recognition (CVPR), 2010 IEEE Conference on Jun, p Kwon J, Lee KM. Visual Tracking Decomposition. Proceeding of CVPR, San Francisco, California. 2010, p Ross DA, Lim J, Lin RS, Yang MH. Incremental Learning for Robust Visual Tracking, International Journal of Computer Vision May; 77(1): Adam A, Rivlin E, Shimshoni I. Robust Fragments-based tracking using the integral histogram IEEE Computer Society Conference on Computer Vision and Pattern Recognition (CVPR 06), 2006 Jun, p Levinshtein A, Stere A, Kutulakos KN, Fleet D, Dickinson S, Siddiqi K. Turbopixels: Fast-super pixels using geometric flows. Pattern Analysis and Machine Intelligence Dec; 31(12): Wang S, Lu H, Yang F, Yang MH. Super pixel Tracking. Proceeding of ICCV, Barcelona, Spain. 2011, p Aref A, Arash R. Presenting an Effective Algorithm for Tracking of Moving Object based on Support Vector Machine. Indian Journal of Science and Technology Aug; 8(17):1-6. Vol 9 (35) September Indian Journal of Science and Technology 5