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3D-DETNet: a single stage video-based vehicle detector
Author(s): Suichan Li; Feng Chen
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Paper Abstract

Video-based vehicle detection has received considerable attention over recent decades. Deep learning based algorithms are effective means for vehicle detection. However, these methods are mainly devised only for static images and applying them for video vehicle detection directly always obtains poor performance. In this work, we propose 3DDETNet: a single-stage video-based vehicle detector integrating 3DCovNet with focal loss, our method has ability to capture temporal information and is more suitable to detect vehicle in video than the other single-stage methods. Our 3D-DETNet takes multiple video frames as input and generate multiple spatial feature maps, then spatial feature maps are fed to sub-model 3DConvNet to capture temporal information which is fed to final fully convolution model for predicting locations of vehicles in video frames. We evaluate our method on UA-DETAC vehicle detection dataset. The experiment results show our method yields better performance and keeps a higher detection speed of 26 fps compared with the other typical methods.

Paper Details

Date Published: 26 July 2018
PDF: 7 pages
Proc. SPIE 10828, Third International Workshop on Pattern Recognition, 108280A (26 July 2018); doi: 10.1117/12.2502012
Show Author Affiliations
Suichan Li, Univ. of Science and Technology of China (China)
Feng Chen, Univ. of Science and Technology of China (China)


Published in SPIE Proceedings Vol. 10828:
Third International Workshop on Pattern Recognition
Xudong Jiang; Zhenxiang Chen; Guojian Chen, Editor(s)

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