Chronological Video Synopsis via Events Rearrangement Optimization

He, Yi; Han, Ji; Sang, Nong; Qu, Zhiguo

doi:10.1049/cje.2016.11.002

Cited by 9 publications

(3 citation statements)

References 13 publications

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“…To evaluate the performance of the surveillance video synopsis method based on STO proposed in this paper, we compare it with the video synopsis method IV, 16 PSO, 22 CE, 26 and CF 27 method. IV method is the latest method in the field of surveillance video synopsis, with strong representativeness.…”

Section: Experimental Results and Analysismentioning

confidence: 99%

“…To strictly preserve the chronological order between moving objects and reduce collisions at the same time, He et al 26 proposed a spatio-temporal events rearrangement optimization algorithm, which integrates temporal model, density model, and collision model. Subsequently, scholars also put forward some approaches to solve the collision artifact problem.…”

Section: Video Synopsis Methods Considering Collisionmentioning

confidence: 99%

“…Several approaches 26 – 29 are presented to settle down the collision artifacts and chronological disorder problems. Although these methods can effectively solve the problem of collision artifacts, they change the chronological order of objects and expands the background to the area without activities, which affect the viewer’s understanding of the scene.…”

Section: Introductionmentioning

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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