Pedestrian violence detection based on optical flow energy characteristics

Guo, Zhu Ming; Wu, Feipeng; Chen, Haishan; Yuan, Junying; Cai, Canzeng

doi:10.1109/icsai.2017.8248479

Cited by 13 publications

(9 citation statements)

References 4 publications

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“…However, they received error rate of 12% as their proposed Oriented VIolent Flows (OViF) could detect violence in crowded scenarios only which demands more robust validation. Research in [28] received accuracy rate of 72% using histogram of the computed optical flow energy values. However, they received high error rate of 28% due to inability of their method to perform under discontinuous and fast motion.…”

Section: Comparison With Previous Research Resultsmentioning

confidence: 99%

“…Research in [28] received accuracy rate of 72% using Shi-Tomasi corner detection and histogram of the computed optical flow energy values. They received error rate of 28% using 0.025 second due to the usage of corner.…”

Section: E Analysis and Discussionmentioning

confidence: 99%

“…However, their proposed method could detect violence only in crowded scenes. Research in [28] proposed a method where corner joints of pictures are detected using Shi-Tomasi corner detection algorithm. They used optical flow parameter which was calculated using Lucas-Kanade pyramid optical flow algorithm for violence detection.…”

Section: Background Studymentioning

confidence: 99%

“…Besides, motion pattern based on moment features extraction is used to reduce background complexity in case of similar colored objects by implicating weighted average of pictorial intensities. Optical flow energy characteristics [28] Oriented VIolent Flows (OViF) [27] CNN with long short-term memory (CNN-LSTM) [20] Local Binary Pattern (LBP) [24] Bag-of-Words framework [25] Grey Level Cooccurrence Matrix (GLCM) [23] Deep Neural Network [21] Decision Tree-SVM (DT-SVM) [22] Acceleration Measure Vectors (AMV) [26] www.ijacsa.thesai.org…”

Section: Background Studymentioning

confidence: 99%

“…Various performance metrics are used to validate the proposed methodology, i.e. accuracy rate [22,35,36], error [27,37,38], computation time [28,39,40] and frame rate [20,41,42,43,44].…”

Section: A Hardware and Software Set Upmentioning

confidence: 99%

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Moment Features based Violence Action Detection using Optical Flow

Saif¹,

Mahayuddin²

2020

IJACSA

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Instantaneous detection of violence is still an unsolved research problem although artificial intelligence lives its prosperous years. The severity of injury causes due to violence can be minimized by detecting violence in real time demands for effective violence detection. Various methods were previously proposed for violence detection which could not provide robust results due many challenges, i.e. noise, motion estimation, lack of appropriate feature selection, lack of effective classification approach, complex background and variations in illumination. This research proposes an efficient method for violence detection using moment features to use motion patterns to facilitate detection in each frame and provides smaller area as region of interest. This means probability for extraction of motion intensity is getting lost because of same colored object in the background is reduced and thus minimizes background complexity. After that, proposed method uses optical flow to calculate angles and linear distances in each frame. In this context, if there is any frame loss due to noise or illumination variation, proposed method uses Kalman filter to process that frame by illuminating noise. Finally, decision for violence is determined using random forest classifier from single feature vector by generating a set of probabilities for each class. Proposed research performed extensive experimentation where accuracy rate of 99.12% was achieved using frame rate of 35 fps which is higher comparing with previous research results. Experimental results reveal the effectiveness of the proposed methodology.

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Section: Comparison With Previous Research Resultsmentioning

confidence: 99%

Section: E Analysis and Discussionmentioning

confidence: 99%

Section: Background Studymentioning

confidence: 99%

Section: Background Studymentioning

confidence: 99%

“…Various performance metrics are used to validate the proposed methodology, i.e. accuracy rate [22,35,36], error [27,37,38], computation time [28,39,40] and frame rate [20,41,42,43,44].…”

Section: A Hardware and Software Set Upmentioning

confidence: 99%

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