Vision‐Based Bicycle Detection Using Multiscale Block Local Binary Pattern

Abstract

Bicycle traffic has heavy proportion among all travel modes in some developing countries, which is crucial for urban traffic control and management as well as facility design. This paper proposes a real-time multiple bicycle detection algorithm based on video. At first, an effective feature called multiscale block local binary pattern (MBLBP) is extracted for representing the moving object, which is a well-classified feature to distinguish between bicycles and nonbicycles; then, a cascaded bicycle classifier trained by AdaBoost algorithm is proposed, which has a good computation efficiency. Finally, the method is tested with video sequence captured from the real-world traffic scenario. The bicycles in the test scenario are successfully detected.

Automated class identification of modes of travel in shared spaces: a case study from India

“…Pattern features from video images were also used to discriminate between cyclists and non-cyclists in [29]. Li et al [30] proposed a novel detection approach based on deep learning based method to recognise pedestrians and cyclists in traffic scenes.…”