Object-based video synopsis approach using particle swarm optimization

Moussa, Mona M.; Shoitan, Rasha

doi:10.1007/s11760-020-01794-1

Cited by 15 publications

(11 citation statements)

References 23 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 preserve interaction among the moving objects, Yumin et al 6 . defined four object relationships between original video and synopsis and proposes a trajectory mapping model to obtain the starting time of all tubes, and Moussa and Shoitan 22 deemed that there are two types of tube relations, which are the peer relation and the intersection relation, and proposes to solve the energy minimization function by PSO. Generally speaking, the method of defining the interaction type to solve the video synopsis is relatively computationally expensive.…”

Section: Related Workmentioning

confidence: 99%

“…Because the technology shifts the object in the temporal domain to get a more compact synopsis video, it brings some problems. 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%

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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: Experimental Results and Analysismentioning

confidence: 99%

Section: Related Workmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

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Surveillance video synopsis based on spatio-temporal offset

2023

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“…It was found that the particle swarm algorithm (PSO) could reach the global minimum solution, but with a lower computational cost. The PSO was used to solve the energy minimization function and to generate a synopsis video [ 20 ]. However, offline optimization methods are complicated and time-consuming.…”

Section: Related Workmentioning

confidence: 99%

Real-Time Video Synopsis via Dynamic and Adaptive Online Tube Resizing

Liao

Liu

Cai

2022

Sensors

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Nowadays, with the increased numbers of video cameras, the amount of recorded video is growing. Efficient video browsing and retrieval are critical issues when considering the amount of raw video data to be condensed. Activity-based video synopsis is a popular approach to solving the video condensation problem. However, conventional synopsis methods always consists of complicated and pairwise energy terms that involve a time-consuming optimization problem. In this paper, we propose a simple online video synopsis framework in which the number of collisions of objects is classified first. Different optimization strategies are applied according to different collision situations to maintain a balance among the computational cost, condensation ratio, and collision cost. Secondly, tube-resizing coefficients that are dynamic in different frames are adaptively assigned to a newly generated tube. Therefore, a suitable mapping result can be obtained in order to represent the proper size of the activity in each frame of the synopsis video. The maximum number of activities can be displayed in one frame with minimal collisions. Finally, in order to remove motion anti-facts and improve the visual quality of the condensed video, a smooth term is introduced to constrain the resizing coefficients. Experimental results on extensive videos validate the efficiency of the proposed method.

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“…They deduced that the method based on GA outperforms the one based on SA in terms of information loss and time consumption. Moussa and Shoitan [ 15 ] utilized particle swarm optimization to arrange the object tubes using an energy minimization function to decrease the collision, preserve the chronological order, and relate the objects.…”

Section: Introductionmentioning

confidence: 99%

User Preference-Based Video Synopsis Using Person Appearance and Motion Descriptions

Shoitan

Moussa

Gharghory

et al. 2023

Sensors

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During the last decade, surveillance cameras have spread quickly; their spread is predicted to increase rapidly in the following years. Therefore, browsing and analyzing these vast amounts of created surveillance videos effectively is vital in surveillance applications. Recently, a video synopsis approach was proposed to reduce the surveillance video duration by rearranging the objects to present them in a portion of time. However, performing a synopsis for all the persons in the video is not efficacious for crowded videos. Different clustering and user-defined query methods are introduced to generate the video synopsis according to general descriptions such as color, size, class, and motion. This work presents a user-defined query synopsis video based on motion descriptions and specific visual appearance features such as gender, age, carrying something, having a baby buggy, and upper and lower clothing color. The proposed method assists the camera monitor in retrieving people who meet certain appearance constraints and people who enter a predefined area or move in a specific direction to generate the video, including a suspected person with specific features. After retrieving the persons, a whale optimization algorithm is applied to arrange these persons reserving chronological order, reducing collisions, and assuring a short synopsis video. The evaluation of the proposed work for the retrieval process in terms of precision, recall, and F1 score ranges from 83% to 100%, while for the video synopsis process, the synopsis video length compared to the original video is decreased by 68% to 93.2%, and the interacting tube pairs are preserved in the synopsis video by 78.6% to 100%.

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Object-based video synopsis approach using particle swarm optimization

Cited by 15 publications

References 23 publications

Surveillance video synopsis based on spatio-temporal offset

Surveillance video synopsis based on spatio-temporal offset

Real-Time Video Synopsis via Dynamic and Adaptive Online Tube Resizing

User Preference-Based Video Synopsis Using Person Appearance and Motion Descriptions

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