  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.
