Coupled multi-object tracking and labeling for vehicle trajectory estimation and matching
Efficient detection and tracking of moving objects in real life conditions is a very challenging research issue, mainly due to occlusions, illumination variations, appearance (disappearance) of new (existing) objects and overlapping issues. In this paper, we address these difficulties by incorporating non-linear and recursive identification mechanisms in motion-based detection and tracking algorithms. Non-linearity allows correct identification of object of complex visual properties while the adaptability makes the proposed scheme able to update its behaviour to the dynamic environmental changes. In addition, in this paper, we introduce the concept of polar spectrum which is a measure for determining the deviation of a vehicle trajectory from an ideal trace. The proposed methods (object tracking and trajectory matching) are applied in survey engineering problems dealing with safe design road turns. In particular, the automatically detected trajectory of a moving vehicle is compared with the ideal trace, through the polar spectrum measure, to determine the safety of a road turn. This trace is also compared with the one manually derived using photogrammetric algorithms and a small error is obtained verifying the efficiency of the method.
motion-based tracking non linear object labelling neural networks