Scene Adaptive Online Surveillance Video Synopsis via Dynamic Tube Rearrangement Using Octree

Yang, Yoonsik; Kim, Haksub; Choi, Heeseung; Chae, Seungho; Kim, Ig-Jae

doi:10.1109/tip.2021.3114986

Cited by 9 publications

(3 citation statements)

References 47 publications

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“…Object tubes and video clips are rearranged to generate synopsis videos. Yang et al 24 proposed a sceneadaptive online video synopsis method using dynamic pipe rearrangement of octrees, which aggregates input videos by analyzing scene features and determining effective spatio-temporal 3D structures. The method can cope with more diverse surveillance video environment.…”

Section: Video Synopsis Methods In Complex Scenesmentioning

confidence: 99%

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Surveillance video synopsis based on spatio-temporal offset

2023

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.With the wide popularity of surveillance cameras, video synopsis technology has become a research hotspot. The existing methods of surveillance video synopsis usually summarize the input video by shifting the object tube in the video on the time axis, which ignore the serious collision artifacts and chronological disorder between moving objects. To solve these problems, we propose a surveillance video synopsis methodology called “surveillance video synopsis based on spatio-temporal offset (STO)” that can simultaneously shift the moving object in the temporal domain and spatial domain. First, object detection and tracking algorithms are used to extract the object tube from the input video. Two collision relations are proposed by analyzing relationship between tubes to classify collision artifacts. Then, we present two spatial offset cases to find the optimal spatial offset of the object tube. Finally, an adaptive optimization frame density model is proposed to analyze the optimal temporal offset of the object tube. Simultaneously, the object tube and the background are stitched according to the STO to generate the synopsis video. Extensive experimental results demonstrate the effectiveness of the proposed method in improving frame compression rate and alleviating collision artifacts.

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Section: Video Synopsis Methods In Complex Scenesmentioning

confidence: 99%

“…On the one hand, during the shifting operation of the object tube in the time axis, collision artifact will be generated 22 – 24 On the other hand, the new time label of the object tubes tends to change the chronological order of the objects via events rearrangement optimization, 25 called CE method.…”

Section: Introductionmentioning

confidence: 99%

Surveillance video synopsis based on spatio-temporal offset

2023

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“…The crowd video analysis methods are divided into three categories: microscopic modeling, macroscopic representation, and the recognition of crowded incidents. 9 The investigation of anomalous occurrence has depicted a prospective effect in the previous decade based on advancements in relevant areas like statistics analysis, AI, soft computing, mathematical modeling, and digital image signal processing. [10][11][12] Many researchers suggest a wide range of programmed tools for anomaly detection.…”

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confidence: 99%

Artificialintelligence‐basedremote monitoring system for automated anomaly detection in wireless sensor networks

Govardhan¹,

Surendranath

Sundararajan³

2022

Concurrency and Computation

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In recent decades, wireless sensor networks (WSNs) are employed in the remote monitoring system which experiences a huge delay due to the excessive time consumption by the image processing techniques. The existing video surveillance system is unable to detect the anomaly accurately and falls short in classifying the routine activity and the anomaly activities. Therefore, an efficient anomaly detection model is necessitated for identifying the anomaly behavior of malicious nodes in the WSN environment. This research work introduces a novel artificial intelligence (AI)-based remote monitoring system for WSN. Primarily, the online video frames are retrieved from the wireless video sensors where the frames are generated by segmenting the entire stream of video as a constant-sized structural frame. The sequence/stream of video segments is fed into the proposed AI framework to detect anomaly activities. It has multiple levels of hidden layers to compare the features of the input captured image with the preloaded reference image. Furthermore, a 5-fold cross-validation approach has been inspected to validate the efficacy of the suggested AI model. The performance results evident that the proposed AI system acquires a better prediction accuracy of 98.2% and precision of 97.3% than the existing monitoring systems.

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