Summary of Sparse Sampling Is All You Need For Fast Wrong-way Cycling Detection in Cctv Videos, by Jing Xu et al.
Sparse Sampling is All You Need for Fast Wrong-way Cycling Detection in CCTV Videos
by Jing Xu, Wentao Shi, Sheng Ren, Pan Gao, Peng Zhou, Jie Qin
First submitted to arxiv on: 12 May 2024
Categories
- Main: Computer Vision and Pattern Recognition (cs.CV)
- Secondary: Artificial Intelligence (cs.AI)
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| Summary difficulty | Written by | Summary |
|---|---|---|
| High | Paper authors | High Difficulty Summary Read the original abstract here |
| Medium | GrooveSquid.com (original content) | Medium Difficulty Summary This paper tackles a critical issue in transportation: wrong-way cycling, which poses significant risks to both cyclists and other road users. To detect and mitigate this problem, the authors propose a novel method called WWC-Predictor that leverages both detection-based and orientation-based information from CCTV videos. The approach is designed to efficiently solve the problem of detecting wrong-way cycling ratios in video sequences, addressing the inefficiencies of direct tracking methods. The proposed benchmark dataset consists of 35 minutes of video sequences with minute-level annotation. The WWC-Predictor method achieves an average error rate of only 1.475% while taking only 19.12% GPU time compared to straightforward tracking methods under the same detection model. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary This paper helps solve a big problem in transportation: wrong-way cycling, which is super risky for both cyclists and other drivers. The authors created a new way to detect this problem using camera videos. Their method looks at two types of information: what’s happening in the video (detection-based) and how the bike is oriented (orientation-based). This helps them quickly figure out when someone is riding their bike the wrong way on the road. They tested their method on 35 minutes of video and got really good results, with only a tiny mistake rate. |
Keywords
* Artificial intelligence * Tracking
