Data association in multiple object tracking: A survey of recent techniques

The advances of Visual object tracking tasks in computer vision have enabled a growing value in its application to video surveillance, particularly in a traffic scenario. In recent years, significant attention has been made for the improvement of multiple object tracking frameworks to be effective in real-time while maintaining accuracy and generality. By breaking down the tasks involved in a Multiple Object Tracking framework based on the Tracking-By-Detection approach — an extension of simply detecting and identifying objects, further involved solving a filtering problem by defining a similarity function to associate objects. Hence, this paper focuses on the task of data association via uniquely defined similarity functions and filters only where we review current literature about these techniques which have been used to advance the performance in MOT for vehicle and pedestrian scenarios. While there is difficulty in classifying the quantitative results for the association task only within a proposed MOT framework, our study tries to outline the fundamental ideas put forward by researchers and compare results in a theoretically qualitative approach. Tracking methods are reviewed by categories based on legacy techniques like Probabilistic and Hierarchical methods, followed by an analysis of new approaches and hybrid models. The models identified in each category are further analysed based on performance in stability, accuracy, robustness, speed and computational complexity to derive an understanding of which direction the research within the data association level is strong and which is lacking. Our review further aims to identify the successful models applied to recognize the weaknesses for future improvement.