AMF-MSPF: A retrospective analysis with online object tracking algorithms

Surveillance systems employ object tracking methods, a class of automatic data analysis algorithms, to accurately locate the target or its trajectory under a wide range of constraints in an uncontrolled environment. The best object tracking algorithm should be able to track under a range of constraints over long periods of time. Moreover, since object tracking algorithms are preliminary stages to these higher-level applications, they must be assigned a fraction of system resources to save computational power for more intensive higher-level decision -making tasks. Object tracking algorithms depend on a pipeline of components such as preprocessing stage, object detection, feature extraction/selection and fusion, and the search mechanism for object tracking. These multi-dimensional components are the tuning parameters for providing a balance between robustness, accuracy, and real-time performance of surveillance systems. In this review paper, the focus is on the detection-free class of tracking, where unlike the track-by-detection and end-to-end tracking, the object is initialized manually. The first aim of this review study is: a literature review on the multi-dimensional research trends with a focus on improving online object-tracking algorithms, and the second part is an experimental evaluation of our pre-viously proposed Adaptive Multi-Feature Framework for MSPF (AMF-MSPF). AMF-MSPF is an MSPF-based adaptive framework that works under an online feature selection mechanism. However, a limited sequence of videos was used from the CAVIAR and PETS datasets to evaluate the AMF-MSPF. Therefore, we felt that an extensive evaluation and comparison, with online state-of-the-art tracking methods in the detection-free cate-gory, are necessary on a richer dataset to thoroughly investigate the effectiveness of AMF-MSPF and highlight its pitfalls. Twenty sequences were selected from the Amsterdam Library of Ordinary Video (ALOV++), a popular dataset with the tracking community, were selected to compare the results of the AMF-MSPF with reference methods in the online category of tracking. ALOV++ is a publically available dataset that is popular for its broad constraints, and more importantly the results of the reference methods are also publically available on this dataset. While the extensive experimentation has demonstrated the effectiveness of the AMF-MSPF against many challenging scenarios, nevertheless, this experimental analysis has highlighted some performance measures where the AMF-MSPF framework fell short of achieving even moderately satisfactory results. This research paper points toward these observations to caution researchers and advise them on what future research should concentrate on while developing generalized real-time tracking systems that would be effective under many scenarios. This research would benefit researchers and engineers working on surveillance systems to identify and tune these parameters for performance gains.

Automated summary

Surveillance systems use object tracking methods to locate targets accurately. Object tracking algorithms need to track under various constraints for a long time and allocate system resources for higher-level tasks. This study focuses on detection-free tracking and evaluates the AMF-MSPF algorithm through experiments while highlighting some areas for improvement.