IMPROVING SPATIO-TEMPORAL FEATURE EXTRACTION TECHNIQUES AND THEIR APPLICATIONS IN ACTION CLASSIFICATION. Maral Mesmakhosroshahi, Joohee Kim

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1 IMPROVING SPATIO-TEMPORAL FEATURE EXTRACTION TECHNIQUES AND THEIR APPLICATIONS IN ACTION CLASSIFICATION Maral Mesmakhosroshahi, Joohee Kim Department of Electrical and Computer Engineering Illinois Institute of Technology Chicago, Illinois ABSTRACT Space-time feature extraction is a recent and popular method used for action recognition. This paper presents a new algorithm to improve the robustness of spatio-temporal feature extraction techniques against the illumination and scale variations. Most of the interest point detectors are sensitive to illumination variations that may cause serious problems in action recognition by finding wrong keypoints. A method is proposed to make the 3-D Harris corner detector robust to illumination changes. Illumination invariance is achieved by applying a contrast stretching function to the video to find the proper intensity level for each pixel. A non-uniform binning method is also proposed to make the 3-D extension of the well-known SIFT descriptor more reliable and robust to scale changes by forming orientation histograms which concentrate on the regions near the interest points. Bag of features technique is used for classifying actions provided by the KTH dataset and the results demonstrate that our proposed method outperforms the original 3-D corner detector and SIFT descriptor. Index Terms SIFT, Harris corner detector, nonuniform binning, illumination invariance, scale invariance, interest point detector, feature descriptor. 1. INTRODUCTION Human action recognition is an important and growing topic in semantic video understanding with many applications in different areas such as surveillance systems, video indexing and human-computer interaction. The aim of action recognition is to automatically detect ongoing events from video data. Human activities consist of a group of human and object movements which have a semantic meaning and action recognition tries to find and classify these movements in video data. Feature extraction is the first step of any recognition problem. Several methods have been proposed to represent features in video such as shape-based and motion-based feature representation, but using the popular bag of words technique in action recognition and eliminating the feature tracking step motivated the researchers to extend the idea of interest points and feature vectors to spatio-temporal domain by considering the video as a 3-D data and extracting features directly from it. Action recognition algorithms that are based on spatiotemporal features are usually consisted of two major steps of feature extraction and action recognition as shown in Fig. 1. Fig. 1. Block diagram for action recognition. Several methods have been proposed for space-time interest point detection and feature description. 3-D interest point detectors usually find different types of interest points by maximizing a response function. One of the interest point detectors is Hessian detector proposed by Willems [1] that is an extension of 2-D blob detection methods [2] and measures the determinant of the Hessian matrix as response function. Dollar [3] introduced a new response function based on the Gabor filter in temporal domain and 2-D Gaussian smoothing filter in spatial domain. Dense sampling by Wang [4] is another way of localizing features that extracts 3-D patches at different scales. One of the popular types of interest points in image and video is corner. Harris and Stephan [5] proposed the Harris corner detector in spatial domain and Laptev and Lindeberg [6] successfully extended it to space-time. In this method, space-time corners can be found by convolving the video data with the 3-D Gaussian function and then calculating the second moment matrix using first order derivatives, spatial and temporal scales σ and τ and 3-D Gaussian kernel. Several improvements are proposed in [6, 7] to make the 3-D

2 corner detector robust to scale variations and camera movement using Galilean transformations. Most of the 2-D and 3-D interest point detectors are sensitive to illumination variations. To make 2-D corner detector robust to illumination, a method has been proposed in [8] which applies a contrast stretching function to the image before finding interest points but spatio-temporal interest point detectors are still not robust to illumination changes. Feature descriptors use information about the shape and motion around the interest point to form a vector representing the interest region. Dollar [3] proposed Cuboid descriptor as a 3-D feature descriptor. General idea of this descriptor is to extract cuboids around each interest point detected and use them for describing local regions. The size of these cuboids should be set to contain most of the important information related to their interest points. Willems [1] proposed extended-surf (ESURF) as an extension of the 2-D SURF descriptor and computes Haar wavelets to form a feature vector. Another spatio-temporal feature descriptor is HOG/HOF by Laptev [9] that uses histogram of gradients (HOG) in spatial direction and histogram of optical flow (HOF) around the interest point. Klaser [10] proposed HOG3D as an extension of the well-known scale invariant feature transform (SIFT) descriptor [11] and is based on the histogram of gradient orientations using polyhedrons. Scovanner [12] proposed 3-D SIFT descriptor as a space-time extension of SIFT. In this method similar to the 2-D SIFT, local orientation histograms are used to describe the region for each interest point. In the spatiotemporal SIFT we have two parameters θ and φ for each pixel which represent the gradient. Parameter φ in this case represents the angle away from the 2-D gradient, therefore, it will be in the range ( π 2, π 2 ). In the 3-D SIFT descriptor only the orientation assignment is extended to space-time and the first step of Lowe s method which makes the SIFT descriptor scale-invariant using difference of Gaussian (DoG) scale space is not applicable here. Therefore, SIFT descriptor is not scale-invariant in space-time. Even in 2-D SIFT, scale invariance cannot be achieved completely. In [13] it is shown that scale invariance of 2-D SIFT is related to the number of images in the scale space and by computing more images we have a better accuracy. However, increasing the number of images will cause higher computational cost which is not desirable. Coi also proved that by changing scale of the image, the error caused by the quantization of the sub-regions can affect the scale invariance of the 2-D SIFT descriptor and solved this problem by proposing the irregular histogram binning method. As discussed above, there exist some limitations in extracting features in spatio-temporal domain such as variation in scale and illumination which can affect the accuracy of action recognition process. In this paper, two methods are proposed to improve the robustness of the 3-D Harris corner detector and 3-D SIFT descriptor against the illumination and scale variations. The structure of the paper is as follows: In Section 2, the proposed algorithm for improving the 3-D Harris corner detector and 3-D SIFT descriptor is explained. Section 3 shows the experimental results of action classification using our proposed algorithm compared to the original methods and Section 4 concludes the paper. 2. PROPOSED ALGORITHM 2.1. Interest point detection The most popular interest point detector in spatio-temporal domain is the 3-D Harris corner detector. Several improvements have been proposed to make this interest point detector robust against scale changes and camera movements but it still fails when we have illumination variations. The reason is that in some situations, the corners become weak because of the similarity between the intensity level of the foreground and background. Fig. 2 shows some example of the frames that wrong points are detected as corners. All of the corners are detected at scales σ 2 = 4 and τ 2 = 4 and k is set to (a) (b) (c) Fig. 2. Example frames from (a) boxing, (b) hand waving, and (c) running actions from KTH dataset [14] where the 3-D Harris corner detector [6] fails to find correct interest points. Motivated by the work proposed in [8], we use a contrast stretching function in spatio-temporal domain to make the interest point detector robust against illumination changes. To have stronger corners in space-time, a sigmoid function defined in Eqn. (1) is applied to the normalized video as a 3-D data to stretch the intensity level of the video. f c (I(x, y, t)) = exp ( γ(i(x, y, t) c)). (1) In Eqn. (1) c is the contrast center, γ is the slope of the sigmoid function and I(x, y, t) is the pixel intensity within the range of [0,1]. Changing the intensity level of the video frames helps us to have stronger corners in both spatial and temporal domains. By finding the Harris response function as a function of c in range of [0,1] for each pixel as in Eqn. (2), the maximum value of this function is considered as a cornerness measure. A(x, y, t) = g(.; σi 2, τi 2 ) L 2 cx L cx L cy L cx L ct L cx L cy L 2 cy L cy L ct L cx L ct L cy L ct L 2 ct, (2)

3 (a) (b) Fig. 3. Example of the interest points detected by (a) our proposed algorithm and (b) the original 3-D corner detector [6]. where I c (x, y, t) = f c (I(x, y, t)) and L cx = x (I c ). The corner function also can be found as follows: R(x, y, t) = deta(x, y, t) k(tracea(x, y, t)) 3. (3) In the original 3-D corner detector, the number of interest points can be changed based on the threshold we set for the local maxima. In some cases when there is not enough difference between the intensity level of the object and the background, by using a higher threshold we may lose some corners. On the other hand, having a large number of interest points will cause a huge computational cost. If we apply the sigmoid function to the video, we can have a reasonable number of interest points without missing any corner. Fig. 3 shows a comparison of the interest points detected by the original corner detector and our proposed method Feature description As discussed previously, 3-D extension of the SIFT descriptor is not scale-invariant. In this paper, a non-uniform binning method is proposed for the 3-D SIFT descriptor as an extension of irregular histogram binning in the 2-D SIFT to have a more reliable and scale-invariant feature vector in spatiotemporal domain. In Fig. 4, the binning method in the original SIFT and the proposed method is compared. In this approach, instead of regular subsampling, the region around the interest point is sampled non-uniformly in order to give more weights to the sub-regions near the interest point in the neighborhood region. This sampling method enables us to have a scaleinvariant and valuable feature vector which is very important for an accurate action classification. By using non-uniform grids, feature descriptors can be found in three steps. First, inner regions are sampled and orientation assignments are computed for these regions. For the second set of sub-regions we again start sampling from the center and extract sub-regions twice the first set of subregions. Third set of sub-regions are also twice the previous set and again contains orientation information starting from the center of the whole cube. Fig. 5 shows the regions extracted in each step. In all three steps, orientation histogram can be found using Eqns. (4), (5) and (6). m 3D (x, y, t) = L 2 x + L 2 y + L 2 t (4) θ(x, y, t) = arctan L y L x (5) φ(x, y, t) = arctan L t, (6) L 2 x + L 2 y where L x, L y, and L t are spatio-temporal gradients. By comparing this method of histogram binning with the original uniform gridding, our proposed method has two advantages. First, in this method all of the sampled regions contain important information from the orientation of the inner regions that are near the interest point. By considering the areas near the keypoints, all of the local histograms will be valuable for us, while in the original method, some of the outer sub-regions only contain background information which is not vital in action recognition and thus results in a less strong feature vector. Scale invariance in this method is achieved by comparing the same action in different scales. Specifically, as we get closer to the interest points, they have more information in common and if we focus on these areas, the chance of recognizing them as the same action will be higher. Second, in the original method by sampling a region to sub-regions we will have 64

4 (a) (b) Fig. 4. Histogram binning in (a) the original SIFT descriptor and (b) our proposed method. local histograms, but in our method we have 24 (3 8) local histograms. Thus, the length of the feature vector is much less than that of the original method which is very important in reducing the computational cost and the memory needed for action recognition Dataset Different datasets are available for evaluating the performance of action recognition. We use the KTH dataset [14] for the experiments since our proposed method focuses on illumination and scale invariance of feature extraction. The KTH dataset contains six actions of running, jogging, walking, boxing, hand waving and hand clapping. For each action, 25 sets of videos exist in four conditions that include scale variation, illumination change and view point change. In this paper, 16 sets of the KTH dataset are used for training and testing and 100 frames are considered for each video Action classification Fig. 5. Binning process of the descriptor histograms is done in three steps starting from the smallest sub-regions (left image) to the largest sub-regions (right image). 3. EXPERIMENTAL RESULTS In this section, the simulation results of action recognition using our proposed methods are compared with the results from other existing methods discussed in the previous sections. To compare the results of action classification based on the improvements done in both interest point detection and feature description, different combinations of interest point detectors and feature descriptors are tested using bag of feature and support vector machine (SVM) classifier. Then, confusion matrix and accuracy are computed for each set. At first, raw descriptors must be quantized and clustered to a prespecified number of clusters using a k-means clustering technique. Centers of these clusters are called words and the collection of these words makes the spatio-temporal vocabulary. In this paper, 1000 features are generated for the vocabulary of features. The recognition process is performed by training a classifier using 8 sets of KTH dataset as training examples from the spatio-temporal vocabulary and then classifying actions using another 8 sets which is the test set. SVM classifier with polynomial kernel is used here as a supervised learning technique for training the classifier. Based on the results of classification, confusion matrices are computed for each

5 combination of detector/descriptor and the confusion matrix shows the performance of a classification algorithm. Fig. 6 shows the action classification algorithm based on bag of features. In the experiments, corner detection parameters, σ 2, τ 2 and k are set to 4, 4 and respectively in both original and proposed corner detector. In our proposed corner detector, slope of the sigmoid function, γ is set to 30 and contrast centers are chosen to be between 0 and 1 with step size of 0.1. Parameters for σ 2, τ 2, γ and step size are obtained heuristically. For feature description, feature vectors are formed by extracting a neighborhood around each interest point. (a) (b) Fig. 6. Action classification based on bag of features. Fig. 7 shows the confusion matrix of our proposed method and the original Harris corner detector and SIFT descriptor when the same number of interest points are detected. It can be observed from the confusion matrices that for all of the actions except boxing, our proposed method shows a better performance while combining the original 3-D corner detector and SIFT descriptor causes misclassification most of the actions. Based on the confusion matrices of all of the combinations of interest point detectors and feature descriptors, the accuracy of action classification is computed by Eqn. (7). number of truely classified data Accuracy = total number of classified data (7) Tables 1 and 2 show the average number of interest points and accuracy for each set. Increasing the number of interest points can increase the accuracy of the classification. Therefore, in all cases, a threshold is used for the interest point detector to have the number of interest points in the same range for both of the detectors in order to avoid the effect of the number of the points on accuracy. Simulation results in Table 2 show that the combination of the proposed methods for interest point detection and feature description gives us the highest accuracy while the lowest Fig. 7. Confusion matrices for the combination of (a) proposed corner detector + proposed SIFT descriptor and (b) 3-D corner detector [6] + original SIFT descriptor [12]. Table 1. Average number of interest points for each detector. Method Number of points Proposed corner detector D corner detector [6] 408 accuracy is obtained by combining the original 3-D corner detector [6] and SIFT descriptor [12]. It can also be seen that by choosing the same interest point detector, our proposed feature descriptor outperforms the original SIFT descriptor and by using the same feature descriptor, our proposed interest point detector shows a better accuracy. 4. CONCLUSION In this paper, two methods are proposed for improving interest point detection and feature description in spatio-temporal domain for human action recognition. Applying sigmoid function to the video data before corner detection is the first improvement that makes the interest point detector robust to illumination changes. As shown in the simulation results, by using this method we have more accurate interest points compared to the original corner detector and blob detector. Second method is proposed to improve the accuracy of the 3-D

6 Table 2. Accuracy of the classification based on the confusion matrix for each combination of detector/descriptor. Method Accuracy% Proposed corner detector + proposed SIFT Proposed corner detector + SIFT [12] D corner detector [6] + proposed SIFT D corner detector [6] + SIFT [12] SIFT descriptor in action recognition. Here, the improvement is obtained by forming gradient histograms that have non-uniform bins. The use of this method can increase the accuracy of action recognition by giving more weight to the inner part of the sub-regions in the descriptors. Simulation results show that the proposed methods improve the accuracy of action recognition under varying illumination conditions and changes in video scales. 5. REFERENCES [1] G. Willems, T. Tuytelaars, and L. Van Gool, An efficient dense and scale-invariant spatio-temporal interest point detector, in 08 Proc. 10th European conf. on computer vision: Part II, pp , [9] I. Laptev, M. Marszalek, C. Schuldt, and B. Rozenfeld, Learning realistic human actions from movies, in CVPR, pp. 1 8, Jun [10] A. Klaser, M. Marszalek, and C. Schmid, A Spatiotemporal Descriptor Based on 3D Gradients, in BMVC, Sep [11] D. G. Lowe, Distinctive image features from scaleinvariant keypoints, Intl. Journal of Computer Vision, vol. 2, pp , Winter [12] P. Scovanner, S. Ali, and M. Shah, A 3-dimensional sift descriptor and its application to action recognition, in Proc. 15th intl. conf. on Multimedia, pp , [13] Y. Cui, N. Hasler, T. Thormaehlen, and H. P. Seidel, Scale invariant feature transform with irregular orientation histogram binning, in Proc. ICIAR, pp , [14] C. Schuldt, I. Laptev, and B. Caputo, Recognizing human actions: A local svm approach, in ICPR, pp , Aug [2] T. Lindeberg, Detecting salient blob-like image structures and their scales with a scale-space primal sketch: A method for focus-of-attention, Intl. Journal of Computer Vision, vol. 11, pp , [3] P. Dollar, V. Rabaud, G. Cottrell, and S. Belongie, Behavior recognition via sparse spatio-temporal features, in VS-PETS, pp , Jan [4] H. Wang, M. M. Ullah, A. Klaser, I Laptev, and C. Schmid, Evaluation of local spatio-temporal features for action recognition, in BMVC, Sep [5] C. Harris and M. Stephen, A combined corner and edge detector, in Proc. 4th ALVEY Vision Conf., pp , [6] I. Laptev and T. Lindeberg, Space-time interest points, in Proc. the ninth IEEE Intl. Conference on Multimedia, pp , Apr [7] T. Linderberg, A. Akbarzadeh, and I. Laptev, Galileandiagonalized spatio-temporal interest operators, in ICPR, pp , Aug [8] M. Gevrekci and B.K. Gunturk, Illumination robust interest point detection, Intl. Journal of Computer Vision, vol. 113, pp , Apr

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