Video anomaly detection based on a hierarchical activity discovery within spatio-temporal contexts

In this paper, we propose a method for real-time anomaly detection and localization in crowded scenes. Each video is defined as a set of non-overlapping cubic patches, and is described using two local and global descriptors. These descriptors capture the video properties from different aspects. By incorporating simple and cost-effective Gaussian classifiers, we can distinguish normal activities and anomalies in videos. The local and global features are based on structure similarity between adjacent patches and the features learned in an unsupervised way, using a sparse autoencoder. Experimental results show that our algorithm is comparable to a state-of-the-art procedure on UCSD ped2 and UMN benchmarks, but even more time-efficient. The experiments confirm that our system can reliably detect and localize anomalies as soon as they happen in a video.

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“…Method Time (second per frame) Xua et al [20] Offline Li et al [11] 1.38 Ours 0.04 Table 1. Run time comparison…”

Section: Experimental Results and Comparisonsmentioning

confidence: 99%

Real-time anomaly detection and localization in crowded scenes

Sabokrou

Fathy

Hoseini

et al. 2015

2015 IEEE Conference on Computer Vision and Pattern Recognition Workshops (CVPRW)

160

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“…Adam et al computed the optical flow at a set of fixed spatial locations by probabilistic histograms . The method of estimating the existing parameter model determines whether the optical flow value is less than the empirical threshold . However, most of these methods do not consider optical flow in a dynamic environment.…”

Section: Related Workmentioning

confidence: 99%

Abnormal visual event detection based on multi‐instance learning and autoregressive integrated moving average model in edge‐based Smart City surveillance

Liu

Zhang

et al. 2019

Softw Pract Exp

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Summary The abnormal visual event detection is an important subject in Smart City surveillance where a lot of data can be processed locally in edge computing environment. Real‐time and detection effectiveness are critical in such an edge environment. In this paper, we propose an abnormal event detection approach based on multi‐instance learning and autoregressive integrated moving average model for video surveillance of crowded scenes in urban public places, focusing on real‐time and detection effectiveness. We propose an unsupervised method for abnormal event detection by combining multi‐instance visual feature selection and the autoregressive integrated moving average model. In the proposed method, each video clip is modeled as a visual feature bag containing several subvideo clips, each of which is regarded as an instance. The time‐transform characteristics of the optical flow characteristics within each subvideo clip are considered as a visual feature instance, and time‐series modeling is carried out for multiple visual feature instances related to all subvideo clips in a surveillance video clip. The abnormal events in each surveillance video clip are detected using the multi‐instance fusion method. This approach is verified on publically available urban surveillance video datasets and compared with state‐of‐the‐art alternatives. Experimental results demonstrate that the proposed method has better abnormal event detection performance for crowded scene of urban public places with an edge environment.

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“…The final category is volume-based representation. [23][24][25][26][27][28][29][30][31][32][33][34][35][36][37][38][39][40][41][42] This category of representation is extracted from a three-dimensional (3-D) cuboid or clip without relying on tracking, an interesting point detection, and foreground extraction methods. The volumebased representation is frequently adopted in many anomaly detection works since it can avoid the limitations of the former three types of representations in dynamic and complex scenes.…”

Section: Related Workmentioning

confidence: 99%

Robust and efficient anomaly detection using heterogeneous representations

Xie

et al. 2015

J. Electron. Imaging

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Various approaches have been proposed for video anomaly detection. Yet these approaches typically suffer from one or more limitations: they often characterize the pattern using its internal information, but ignore its external relationship which is important for local anomaly detection. Moreover, the high-dimensionality and the lack of robustness of pattern representation may lead to problems, including overfitting, increased computational cost and memory requirements, and high false alarm rate. We propose a video anomaly detection framework which relies on a heterogeneous representation to account for both the pattern's internal information and external relationship. The internal information is characterized by slow features learned by slow feature analysis from low-level representations, and the external relationship is characterized by the spatial contextual distances. The heterogeneous representation is compact, robust, efficient, and discriminative for anomaly detection. Moreover, both the pattern's internal information and external relationship can be taken into account in the proposed framework. Extensive experiments demonstrate the robustness and efficiency of our approach by comparison with the state-of-the-art approaches on the widely used benchmark datasets.

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Video anomaly detection based on a hierarchical activity discovery within spatio-temporal contexts

Cited by 92 publications

References 37 publications

Real-time anomaly detection and localization in crowded scenes

Real-time anomaly detection and localization in crowded scenes

Abnormal visual event detection based on multi‐instance learning and autoregressive integrated moving average model in edge‐based Smart City surveillance

Robust and efficient anomaly detection using heterogeneous representations

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