Online Detection of Abnormal Events in Video Streams

Hindawi Publishing Corporation Journal of Electrical and Computer Engineering Volume 2013, Article ID 837275, 12 pages http://dx.doi.org/10.1155/2013/837275 Research Article Online Detection of Abnormal Events in Video Streams Tian Wang,1 Jie Chen,2 and Hichem Snoussi1 1 2 Institut Charles Delaunay, LM2S-UMR STMR 6279 CNRS, University of Technology of Troyes, 10004 Troyes, France Observatoire de la Côte d’Azur, UMR 7293 CNRS, University of Nice Sophia-Antipolis, 06108 Nice, France Correspondence should be addressed to Tian Wang; Received 19 September 2013; Accepted 12 November 2013 Academic Editor: Yi Zhou Copyright © 2013 Tian Wang et al. This is an open access article distributed under the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited. We propose an algorithm to handle the problem of detecting abnormal events, which is a challenging but important subject in video surveillance. The algorithm consists of an image descriptor and online nonlinear classification method. We introduce the covariance matrix of the optical flow and image intensity as a descriptor encoding moving information. The nonlinear online support vector machine (SVM) firstly learns a limited set of the training frames to provide a basic reference model then updates the model and detects abnormal events in the current frame. We finally apply the method to detect abnormal events on a benchmark video surveillance dataset to demonstrate the effectiveness of the proposed technique. 1. Introduction Visual surveillance is one of the major research areas in computer vision. In a crowd image analysis problem, the scientific challenge includes abnormal events detection. For instance, Figure 1(a) illustrates a normal scene where the people are walking. In Figure 1(b), all the people are suddenly running in different directions. This dataset imitates panicdriven scenes. Trajectory analysis of objects was described in [1–3]. The moving object was labeled by a blob in consecutive frames, and then a trajectory was produced. The deviation from the learnt trajectories was defined as abnormal events. Tracking based approaches are suitable for the sparse scenes with a few objects. The target might be lost due to occlusion. In [4, 5], abnormal detection approaches which used features encoding motion, texture, and size of the objects were introduced. Local image regions in a video were analyzed by employing background subtraction method; then a dynamic Bayesian network (DBN) was constructed to model normal and abnormal behavior, and finally a likelihood ratio test was applied to detect abnormal behaviors. In [6], a spacetime Markov random field (MRF) model which detected abnormal activities in a video was proposed, mixture of probabilistic principal component analyzers (MPPCA) was adopted to model local optical flow. The prediction is based on probabilistic assumption techniques where an accurate model exists, but there are various situations where a robust and tractable model cannot be obtained; model-free methods are needed to be studied. Spatiotemporal motion features described by the context of bag of video words were adopted to detect abnormal events. In [7], the authors presented an algorithm which monitored optical flow in a set of fixed spatial positions, and constructed a histogram of optical flow. The likelihood of the behavior in a new coming frame concerning the probability distribution of the statistically learning behavior was computed. If the likelihood fell below a preset threshold, the behavior was considered as abnormal. In [8], irregular behavior of images or videos was detected by an inference process in a probabilistic graphical model. In [9, 10], the video pixels were densely sampled to form the feature. These methods are based on the partial information of images, such as small blocks in a frame, without fully exploiting the global information of the feature. In [11–13], spatiotemporal features modeled motion regions of the frame as background, and anomaly was detected by subtracting the newly sample to the background template. These works are similar to the change detection method when the background is not stable. In this paper, the proposed algorithm is composed of two parts. Firstly, a covariance feature descriptor is constructed over the whole video frame, and then a nonlinear one-class 2 Journal of Electrical and Computer Engineering support vector machine algorithm is applied in an online fashion in order to detect abnormal events. The features are extracted based on the optical flow which presents the movement information. Experiments of real surveillance video dataset show that our online abnormal detection techniques can obtain satisfactory performance. The rest of the paper is organized as follows. In Section 2, covariance matrix descriptor of motion feature is introduced. In Section 3, the online one-class SVM classification method is presented. In Section 4, two abnormal detection strategies based on online nonlinear one-class SVM are proposed. In Section 5, we present results of real-world video scenes. Finally, Section 6 concludes the paper. 2. Covariance Descriptor of Frame Behavior The optical flow is a feature which presents the direction and the amplitude of a movement. It can provide important information about the spatial arrangement of the objects and the change rate of this arrangement [14]. We adopt HornSchunck (HS) optical flow computation method in our work. The optical flow of the gray scale image is formulated as the minimizer of the following global energy functional: 2 𝐸 = ∬ [(𝐼𝑥 𝑢 + 𝐼𝑦 V + 𝐼𝑡 ) + 𝛼2 (‖∇𝑢‖2 + ‖∇V‖2 )] 𝑑𝑥𝑑𝑦, (1) where 𝐼 is the intensity of the image, 𝐼𝑥 , 𝐼𝑦 , and 𝐼𝑡 are the derivatives of the image intensity value along the 𝑥, 𝑦, and time 𝑡 dimension, 𝑢 and V are the components of the optical flow in the horizontal and vertical direction, and 𝛼 represents the weight of the regularization term. We introduce the covariance matrix encoding the optical flow and intensity of each frame as the descriptor to represent the movement. The covariance feature descriptor is originally proposed by Tuzel et al. [15] for pattern matching in a target tracking problem. The descriptor is defined as 𝐹 (𝑥, 𝑦, 𝑖) = 𝜙𝑖 (𝐼, 𝑥, 𝑦) , (2) where 𝐼 is the color information of an image (which can be gray, RGB, HSV, HLS, etc.), 𝜙𝑖 is a mapping relating the image with the 𝑖th feature from the image, 𝐹 is the 𝑊 × 𝐻 × 𝑑 dimensional feature extracted from image 𝐼, 𝑊 and 𝐻 are the image width and image height, and 𝑑 is the number of chosen features. For each frame, the feature can be represented as 𝑑 × 𝑑 covariance matrix: C= 1 𝑛 ⊤ ∑ (z − 𝜇) (z𝑘 − 𝜇) , 𝑛 − 1 𝑘=1 𝑘 (3) where 𝑛 is the number of the pixels sampled in the frame, z𝑘 is the feature vector of pixel 𝑘, 𝜇 is the mean of all the selected points, and C is the covariance matrix of the feature vector 𝐹. (...truncated)