Abnormal Event Detection in Crowded Video Scenes

Gnanavel, V. K.; Srinivasan, A.

doi:10.1007/978-3-319-12012-6_48

Cited by 15 publications

(2 citation statements)

References 7 publications

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“…A new motion field named crowd kinetic energy was proposed by jing [18]. In [13], each pixel gets a flow vector by the optical flow between consecutive video frames. Compared with the optical flow, the streak flow in [27] can achieve a more accurate representation of flow crowd motion.…”

Section: Mean Kinetic Violent Flowmentioning

confidence: 99%

“…These can provide excellent and effective information in some benchmark cases, but violent behavior may not be detected in low-resolution video, fast or slow motion video, etc. Hassner et al [13] proposed a descriptor called Violent Flows (ViF) to detect violence in crowded scenes. In [14] proposed a new feature for violence detection that makes full use of the orientation information of optical flow, which is omitted by ViF.…”

Section: Introductionmentioning

confidence: 99%

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Detection of violent crowd behavior based on mean kinetic streak flow

Zhou

2023

Fifth International Conference on Computer Information Science and Artificial Intelligence (CISAI 2022)

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With the frequent occurrence of global security problems, violent crowd behavior endangers public security seriously. Meanwhile, intelligent surveillance video technology can be applied for violent crowd behavior detection as more and more surveillance cameras are installed in public and sensitive areas. In this paper, we propose a novel mean kinetic violent flow (MKViF) algorithm for violent crowd behavior detection by extracting the kinetic energy feature of video flow. Specifically, A is firstly calculating the mean kinetic energy by streak flow of each corner in each frame. Then, we obtain a binary indicator of kinetic energy change by calculating the amplitude change between sequence frames. Finally, the MKViF vector for a sequence of frames is obtained by averaging these binary indicators of each pixel in all frames. Experimental results show that the proposed MKViF algorithm behaves better in classification performance and real-time processing performance (45 frames per second) than the existing algorithms.

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Section: Mean Kinetic Violent Flowmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%