Bicycle Detection Based On Multi-feature and Multi-frame Fusion in low-resolution traffic videos
This addresses bicycle-related accident prevention in China's traffic surveillance systems, but it is incremental as it builds on existing feature fusion and SVM techniques.
The paper tackles bicycle detection in low-resolution traffic videos by proposing a method based on multi-feature and multi-frame fusion, achieving high accuracy and low computational complexity suitable for real-time surveillance.
As a major type of transportation equipments, bicycles, including electrical bicycles, are distributed almost everywhere in China. The accidents caused by bicycles have become a serious threat to the public safety. So bicycle detection is one major task of traffic video surveillance systems in China. In this paper, a method based on multi-feature and multi-frame fusion is presented for bicycle detection in low-resolution traffic videos. It first extracts some geometric features of objects from each frame image, then concatenate multiple features into a feature vector and use linear support vector machine (SVM) to learn a classifier, or put these features into a cascade classifier, to yield a preliminary detection result regarding whether an object is a bicycle. It further fuses these preliminary detection results from multiple frames to provide a more reliable detection decision, together with a confidence level of that decision. Experimental results show that this method based on multi-feature and multi-frame fusion can identify bicycles with high accuracy and low computational complexity. It is, therefore, applicable for real-time traffic video surveillance systems.