Video anomaly detection is essential to distinguish abnormal events in large volumes of surveillance video and can benefit many fields such as traffic management, public security and failure detection. However, traditional video anomaly detection methods are unable to accurately detect and locate abnormal events in real scenarios, while existing deep learning methods are likely to omit important information when extracting features. In order to avoid omitting important features and improve the accuracy of abnormal event detection and localization, this paper proposes a novel method called Multi Chunk Learning based Skip Connected Convolutional Auto Encoder (MCSCAE). The proposed method improves the accuracy of video anomaly detection by obtaining more vital information. In the data sorting phase, non-uniform chunking is proposed to divide the video frame into several chunks of different sizes to avoid obtaining unnecessary information and omitting crucial information. In order to well reflect the abnormal motion of objects in the video, a new feature, the inter frame flow feature, which is obtained by merging inter frame difference and optical flow features, is proposed to extract motion feature. Moreover, in this paper, skip connection in the auto encoder is utilized during the training phase to reduce the reconstruction error between the original frames and the reconstruction frames, so that the reconstruction error can be used to detect abnormal events during testing. Experience on three public datasets verifies the effectiveness and accuracy of our proposed method. Experimental results show that the proposed method can detect and locate abnormal events outperforms other recent methods significantly.
Anomaly detection in surveillance videos is an extremely challenging task due to the ambiguous definitions for abnormality. In a complex surveillance scenario, the kinds of abnormal events are numerous and might co-exist, including such as appearance and motion anomaly of objects, long-term abnormal activities, etc. Traditional video anomaly detection methods cannot detect all these kinds of abnormal events. Hence, we utilize multiple probabilistic models inference to detect as many different kinds of abnormal events as possible. To depict realistic events in a scene, the parameters of our methods are tailored to the characteristics of video sequences of practical surveillance scenarios. However, there is a lack of video anomaly detection methods suitable for real-time processing, and the trade-off between detection accuracy and computational complexity has not been given much attention. To reduce high computational complexity and shorten frame processing times, we employ a variable-sized cell structure and extract a compact feature set from a limited number of video volumes during the feature extraction stage. In conclusion, we propose a real-time video anomaly detection algorithm called MPI-VAD that combines the advantages of multiple probabilistic models inference. Experiment results on three publicly available datasets show that the proposed method attains competitive detection accuracies and superior frame processing speed.
The discovery of gradual moving object clusters pattern from trajectory streams allows characterizing movement behavior in real time environment, which leverages new applications and services. Since the trajectory streams is rapidly evolving, continuously created and cannot be stored indefinitely in memory, the existing approaches designed on static trajectory datasets are not suitable for discovering gradual moving object clusters pattern from trajectory streams. This paper proposes a novel algorithm of gradual moving object clusters pattern discovery from trajectory streams using sliding window models. By processing the trajectory data in current window, the mining algorithm can capture the trend and evolution of moving object clusters pattern. Firstly, the density peaks clustering algorithm is exploited to identify clusters of different snapshots. The stable relationship between relatively few moving objects is used to improve the clustering efficiency. Then, by intersecting clusters from different snapshots, the gradual moving object clusters pattern is updated. The relationship of clusters between adjacent snapshots and the gradual property are utilized to accelerate updating process. Finally, experiment results on two real datasets demonstrate that our algorithm is effective and efficient.
With the aim to meet the requirements of multi-directional choice, the paper raise a new approach to the invariant feature extraction of handwritten Chinese characters, with ridgelet transform as its foundation. First of all, the original images will be rotated to the Radon circular shift by means of Radon transform. On the basis of the characteristic that Fourier transform is row shift invariant, then, the one-dimensional Fourier transform will be adopted in the Radon domain to gain the conclusion that magnitude matrixes bear the rotation-invariance as a typical feature, which is pretty beneficial to the invariant feature extraction of rotation. When such is done, one-dimensional wavelet transform will be carried out in the direction of rows, thus achieving perfect choice of frequency, which makes it possible to extract the features of sub-line in the appropriate frequencies. Finally, the average values, standard deviations and the energy values will form the feature vector which is extracted from the ridgelet sub-bands. The approaches mentioned in the paper could satisfy the requirements from the form automatic processing on the recognition of handwritten Chinese characters.
scite is a Brooklyn-based organization that helps researchers better discover and understand research articles through Smart Citations–citations that display the context of the citation and describe whether the article provides supporting or contrasting evidence. scite is used by students and researchers from around the world and is funded in part by the National Science Foundation and the National Institute on Drug Abuse of the National Institutes of Health.
hi@scite.ai
10624 S. Eastern Ave., Ste. A-614
Henderson, NV 89052, USA
Copyright © 2024 scite LLC. All rights reserved.
Made with 💙 for researchers
Part of the Research Solutions Family.