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As a major type of transportation equipments, bicycles, including electricalbicycles, are distributed almost everywhere in China. The accidents caused bybicycles have become a serious threat to the public safety. So bicycledetection is one major task of traffic video surveillance systems in China. Inthis paper, a method based on multi-feature and multi-frame fusion is presentedfor bicycle detection in low-resolution traffic videos. It first extracts somegeometric features of objects from each frame image, then concatenate multiplefeatures into a feature vector and use linear support vector machine (SVM) tolearn a classifier, or put these features into a cascade classifier, to yield apreliminary detection result regarding whether an object is a bicycle. Itfurther fuses these preliminary detection results from multiple frames toprovide a more reliable detection decision, together with a confidence level ofthat decision. Experimental results show that this method based onmulti-feature and multi-frame fusion can identify bicycles with high accuracyand low computational complexity. It is, therefore, applicable for real-timetraffic video surveillance systems.

Bicycle Detection Based On Multi-feature and Multi-frame Fusion in low-resolution traffic videos