Efficient feature fusion network based on center and scale prediction for pedestrian detection

Zhang, Tao; Cao, Yahui; Zhang, Le; Li, Xuan

doi:10.1007/s00371-022-02528-9

Cited by 5 publications

(1 citation statement)

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“…A CNN is a powerful feature extraction model that has been adopted for a wide variety of detection methods. This research applied a CNN to lidar-based object detection [18]. Here, a CNN is utilized as the backbone network for extracting point cloud features and generating high-quality 3D proposal boxes.…”

Section: Recent Research Based On Deep Sort Influence Maps and Cnnsmentioning

confidence: 99%

Traffic Accident Detection Method Using Trajectory Tracking and Influence Maps

Zhang

Sung

2023

Mathematics

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With the development of artificial intelligence, techniques such as machine learning, object detection, and trajectory tracking have been applied to various traffic fields to detect accidents and analyze their causes. However, detecting traffic accidents using closed-circuit television (CCTV) as an emerging subject in machine learning remains challenging because of complex traffic environments and limited vision. Traditional research has limitations in deducing the trajectories of accident-related objects and extracting the spatiotemporal relationships among objects. This paper proposes a traffic accident detection method that helps to determine whether each frame shows accidents by generating and considering object trajectories using influence maps and a convolutional neural network (CNN). The influence maps with spatiotemporal relationships were enhanced to improve the detection of traffic accidents. A CNN is utilized to extract latent representations from the influence maps produced by object trajectories. Car Accident Detection and Prediction (CADP) was utilized in the experiments to train our model, which achieved a traffic accident detection accuracy of approximately 95%. Thus, the proposed method attained remarkable results in terms of performance improvement compared to methods that only rely on CNN-based detection.

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Section: Recent Research Based On Deep Sort Influence Maps and Cnnsmentioning

confidence: 99%