In crowded scenes, detection and localization of abnormal behaviors is challenging in that high-density people make object segmentation and tracking extremely difficult. We associate the optical flows of multiple frames to capture short-term trajectories and introduce the histogram-based shape descriptor referred to as shape contexts to describe such short-term trajectories. Furthermore, we propose a K-NN similarity-based statistical model to detect anomalies over time and space, which is an unsupervised one-class learning algorithm requiring no clustering nor any prior assumption. Firstly, we retrieve the K-NN samples from the training set in regard to the testing sample, and then use the similarities between every pair of the K-NN samples to construct a Gaussian model. Finally, the probabilities of the similarities from the testing sample to the K-NN samples under the Gaussian model are calculated in the form of a joint probability. Abnormal events can be detected by judging whether the joint probability is below predefined thresholds in terms of time and space, separately. Such a scheme can adapt to the whole scene, since the probability computed as such is not affected by motion distortions arising from perspective distortion. We conduct experiments on real-world surveillance videos, and the results demonstrate that the proposed method can reliably detect and locate the abnormal events in the video sequences, outperforming the state-of-the-art approaches.
In this study, We detect the correspondences among the optical flows across multiple frames to capture short-term trajectories and employ the histogram-based shape descriptor referred to as shape contexts @cite_23 to characterize such short-term trajectories. Then, we model the motion features using the proposed statistical modeling of the K-NN similarities to detect anomalies over time and space.
@cite_23
1999499433 2068611653 914561379 1980807298
This paper presents a novel video descriptor, referred to as Histogram of Oriented Track lets, for recognizing abnormal situation in crowded scenes. Unlike standard approaches that use optical flow, which estimates motion vectors only from two successive frames, we built our descriptor over long-range motion trajectories which is called track lets in the literature. Following the standard procedure, we divided video sequences in spatio-temporal cuboids within which we collected statistics on the track lets passing through them. In particular, we quantized orientation and magnitude in a 2-dimensional histogram which encodes the motion patterns expected in each cuboid. We classify frames as normal and abnormal by using Latent Dirichlet Allocation and Support Vector Machines. We evaluated the effectiveness of the proposed descriptors on three datasets: UCSD, Violence in Crowds and UMN. The experiments demonstrated (i) very promising results in abnormality detection, (ii) setting new state-of-the-art on two of them, and (iii) outperforming former descriptors based on the optical flow, dense trajectories and the social force model. This paper introduces a video representation based on dense trajectories and motion boundary descriptors. Trajectories capture the local motion information of the video. A dense representation guarantees a good coverage of foreground motion as well as of the surrounding context. A state-of-the-art optical flow algorithm enables a robust and efficient extraction of dense trajectories. As descriptors we extract features aligned with the trajectories to characterize shape (point coordinates), appearance (histograms of oriented gradients) and motion (histograms of optical flow). Additionally, we introduce a descriptor based on motion boundary histograms (MBH) which rely on differential optical flow. The MBH descriptor shows to consistently outperform other state-of-the-art descriptors, in particular on real-world videos that contain a significant amount of camera motion. We evaluate our video representation in the context of action classification on nine datasets, namely KTH, YouTube, Hollywood2, UCF sports, IXMAS, UIUC, Olympic Sports, UCF50 and HMDB51. On all datasets our approach outperforms current state-of-the-art results. This paper introduces a state-of-the-art video representation and applies it to efficient action recognition and detection. We first propose to improve the popular dense trajectory features by explicit camera motion estimation. More specifically, we extract feature point matches between frames using SURF descriptors and dense optical flow. The matches are used to estimate a homography with RANSAC. To improve the robustness of homography estimation, a human detector is employed to remove outlier matches from the human body as human motion is not constrained by the camera. Trajectories consistent with the homography are considered as due to camera motion, and thus removed. We also use the homography to cancel out camera motion from the optical flow. This results in significant improvement on motion-based HOF and MBH descriptors. We further explore the recent Fisher vector as an alternative feature encoding approach to the standard bag-of-words (BOW) histogram, and consider different ways to include spatial layout information in these encodings. We present a large and varied set of evaluations, considering (i) classification of short basic actions on six datasets, (ii) localization of such actions in feature-length movies, and (iii) large-scale recognition of complex events. We find that our improved trajectory features significantly outperform previous dense trajectories, and that Fisher vectors are superior to BOW encodings for video recognition tasks. In all three tasks, we show substantial improvements over the state-of-the-art results. We suggest a novel shape contour descriptor for shape matching and retrieval. The new descriptor is called contour points distribution histogram (CPDH) which is based on the distribution of points on object contour under polar coordinates. CPDH not only conforms to the human visual perception but also the computational complexity of it is low. Invariant to scale and translation are the intrinsic properties of CPDH and the problem of the invariant to rotation can be partially resolved in the matching process. After the CPDHs of images are generated, the similarity value of the images is obtained by EMD (Earth Mover's Distance) metric. In order to make the EMD method used effectively for the matching of CPDHs, we also develop a new approach to the ground distance used in the EMD metric under polar coordinates. Experimental results of image retrieval demonstrate that the novel descriptor has a strong capability in handling a variety of shapes.