Appearance-based passenger counting in cluttered scenes with lateral movement compensation

Autonomous passenger counting in public transportation represents an integral part of an intelligent transportation system, as it provides vital information to improve the efficiency and resource management of a public transportation network. However, counting passengers in highly crowded scenes is a challenging task due to their random movement, diverse appearance settings and inter-object occlusions. Furthermore, state-of-the-art methods in this domain rely heavily on additional custom cameras or sensors instead of existing onboard surveillance cameras, which consequently limits the feasibility of such systems for large-scale deployment. Hence, this paper puts forward an enhanced appearance descriptor with lateral movement compensation, which addresses the difficulty in counting passengers bidirectionally in cluttered scenes. We first construct a head re-identification dataset, which is used to train an appearance descriptor. This dataset addresses the absence of a person re-identification dataset, which in turn allows for accurate tracking of passengers in cluttered scenes. Then, a novel technique of applying a fedora counting line is introduced to count the number of passengers entering and exiting a bus. This technique compensates the impact of passengers’ lateral movement, which crucially increases the accuracy of bidirectional passenger counting using onboard bus surveillance cameras. In addition, a real-time implementation of the proposed method, which includes the integration of DeepStream and fedora counting line, is also presented. Experimental results on a challenging test dataset demonstrate that the proposed method outperforms benchmarked techniques with an average counting accuracy of 93.21% for entering and 96.10% for exiting public buses. Furthermore, the proposed system achieves this accuracy at an average frame rate of 16 frames per second, which represents a practical solution to a real-time application.