Coherent event-based surveillance video synopsis using trajectory clustering

Chou, Chien; Lin, Chin Hsien; Chiang, Tzu Hsuan; Chen, Hua-Tsung; Lee, Suh-Yin

doi:10.1109/icmew.2015.7169855

Cited by 8 publications

(4 citation statements)

References 12 publications

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“…This method can achieve a high CR while mitigating the collision and chronological disorder, but it destroyed the spatial position relationship and is unreadable video content. Chou et al 41 proposed a trajectory clustering algorithm that iteratively moves the object tube in time until there is no collision (NC). Nie et al 29 extended the moving trajectory of objects to avoid the collision artifact, which changes the real background of the surveillance video.…”

Section: Video Synopsis Methods Considering Collisionmentioning

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 Considering Collisionmentioning

confidence: 99%

“…Tube optimization rearrangement is the core step of video synopsis 15 – 21 In part III, the surveillance video background and the object tube with a new time label are stitched to generate a synopsis video. Because the technology shifts the object in the temporal domain to get a more compact synopsis video, it brings some problems.…”

Section: Introductionmentioning

confidence: 99%

Surveillance video synopsis based on spatio-temporal offset

2023

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“…The criteria for entry and exit were fixed in the camera. Chou et al [23] selected that the activity beyond the template will be considered abnormal for constructing an event video synopsis. Similarly, Lin et al [24] incorporated a local patch of occurrence to find anomalies, where they used a sequence optimization for extraction and visualizing the activities in the synopsis.…”

Section: Traditional Video Synopsis Methodologiesmentioning

confidence: 99%

“…While stitching the foreground objects creates a collision and merging of the entities. It is a time and memory-intensive task; thus, these methods are insufficient for a longer dynamic video sequence [23].…”

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confidence: 99%

DVS: A Drone Video Synopsis towards Storing and Analyzing Drone Surveillance Data in Smart Cities

Ingle

Kim

2022

Systems

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The commercialization and advancement of unmanned aerial vehicles (UAVs) have increased in the past decades for surveillance. UAVs use gimbal cameras and LIDAR technology for monitoring as they are resource-constrained devices that are composed of limited storage, battery power, and computing capacity. Thus, the UAV’s surveillance camera and LIDAR data must be analyzed, extracted, and stored efficiently. Video synopsis is an efficient methodology that deals with shifting foreground objects in time and domain space, thus creating a condensed video for analysis and storage. However, traditional video synopsis methodologies are not applicable for making an abnormal behavior synopsis (e.g., creating a synopsis only of the abnormal person carrying a revolver). To mitigate this problem, we proposed an early fusion-based video synopsis. There is a drastic difference between the proposed and the existing synopsis methods as it has several pressing characteristics. Initially, we fused the 2D camera and 3D LIDAR point cloud data; Secondly, we performed abnormal object detection using a customized detector on the merged data and finally extracted only the meaningful data for creating a synopsis. We demonstrated satisfactory results while fusing, constructing the synopsis, and detecting the abnormal object; we achieved an mAP of 85.97%.

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A Comprehensive Investigation About Video Synopsis Methodology and Research Challenges

Jagtap

Chopade

2021

Inventive Computation and Information Technologies

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Coherent event-based surveillance video synopsis using trajectory clustering

Cited by 8 publications

References 12 publications

Surveillance video synopsis based on spatio-temporal offset

Surveillance video synopsis based on spatio-temporal offset

DVS: A Drone Video Synopsis towards Storing and Analyzing Drone Surveillance Data in Smart Cities

A Comprehensive Investigation About Video Synopsis Methodology and Research Challenges

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