Radius Nearest Neighbour Based Feature Classification for Occlusion Handling
Kh. Manglem Singh, Vinod Karar, Shashi Poddar · Pattern Recognition and Image Analysis · 2020
Abstract Multiple object tracking (MOT) at road intersections is a major research area in the field of intelligent transportation systems (ITS). In dynamic scenes like urban traffic, there is a variety of moving objects ranging from simple pedestrians to large trucks. Occlusion due to the interaction between these moving objects is one of the major problems in traffic tracking and monitoring at intersections. This paper presents an occlusion handling approach using an efficient feature-based multiple object tracking technique. A considerable amount of research work has been done in the area of occlusion handling but most of the proposed techniques can either handle occlusion of only two objects or fail to segment the individual vehicles throughout occlusion. The proposed approach uses radius nearest neighbour (RNN) classification to group the unmatched feature points along with matched feature points to update the object model. In order to validate the efficiency of this method, it is tested on four different urban traffic sequences from the Urban Tracker dataset and four traffic sequences from the Ko-PER Intersection Dataset. Experimental results depict the tracking approach of RNN based scheme compared to the other schemes for better occlusion handling and individual object segmentation.