Object tracking has become the cornerstone of many computer vision applications. Numerous object tracking methods have surfaced in the research community which are intended for high level applications such as automatic data analysis for activity recognition. Most of the methods are either too constrained in the context of the given application or they are costly in terms of computations to meet the real-time requirements. For example, Mean-Shift (MS) has rose to prominence due to its ease of implementation and robustness to various deformations however it fails to track objects with small sizes, fast motion and full occlusion. On the other hand, Particle Filter (PF) is known for its efficiency, accuracy and robustness to small sizes, fast object motion and full occlusion however it is heavily influenced by the number of particles, besides the sample degeneracy and impoverishment problems. Decoupling the disadvantages of both the methods gave birth to a new era of modern trackers known as hybrid systems that are more efficient, accurate and robust to the aforementioned constraints.A few survey papers on object tracking has been published in the scientific circles during the last decade however we feel that this popular integration of MS and PF is still unregistered.

In recent years, a plethora of algorithms have been devised for efficient human activity recognition. Most of these algorithms consider basic human activities and neglect postural transitions because of their subsidiary occurrence and short duration. However, postural transitions assume a significant part in the enforcement of an activity recognition framework and cannot be neglected. This work proposes a hybrid multi-model activity recognition approach that employs basic and transition activities by utilizing multiple deep learning models simultaneously. For final classification, a dynamic decision fusion module is introduced. The experiments are performed on the publicly available datasets. The proposed approach achieved a classification accuracy of 96.11% and 98.38% for the transition and basic activities, respectively. The outcomes show that the proposed method is superior to the state-of-the-art methods in terms of accuracy and precision.

Object tracking is a computer vision task deemed necessary for high-level intelligent decision-making algorithms. Researchers have merged different object tracking techniques and discovered a new class of hybrid algorithms that is based on embedding a meanshift (MS) optimization procedure into the particle filter (PF) (MSPF) to replace its inaccurate and expensive particle validation processes. The algorithm employs a combination of predetermined features, implicitly assuming that the background will not change. However, the assumption of fully specifying the background of the object may not often hold, especially in an uncontrolled environment. The first innovation of this research paper is the development of a dynamically adaptive multi-feature framework for MSPF (AMF-MSPF) in which features are ranked by a ranking module and the top features are selected on-the-fly. As a consequence, it improves local discrimination of the object from its immediate surroundings. It is also highly desirable to reduce the already complex framework of the MSPF to save resources to implement a feature ranking module. Thus, the second innovation of this research paper introduces a novel technique for the MS optimization method, which reduces its traditional complexity by an order of magnitude. The proposed AMF-MSPF framework is tested on different video datasets that exhibit challenging constraints. Experimental results have shown robustness, tracking accuracy and computational efficiency against these constraints. Comparison with existing methods has shown significant improvements in term of root mean square error (RMSE), false alarm rate (FAR), and F-SCORE.

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