A powerful approach to real-time mobile objects tracking in crowded environments utilizing 3D video analysis of fixed camera, particle filter and neural network

• A powerful approach in the area of real-time mobile objects tracking in crowded environments is proposed.
• 3D video frames are analyzed to track a number of real-time mobile objects.
• Real complex situations in the presence of occlusion, overlapping and various shifts through PETS-09 database are chosen to carry out the experimental tests.
• The results are a development of probabilistic estimation theory via particle filter, since a novel neural network is investigated.
• All the new features of mobile objects are analyzed to improve the investigated results.

A powerful approach in the area of real-time mobile objects tracking in crowded environments, utilizing 3D video frames analysis is now taken into real consideration, as a candidate to be improved. The method presented here is able to track a number of real-time mobile objects in the real complex situations in the presence of occlusion, overlapping and various shifts. This is a development of probabilistic estimation theory via particle filter. In one such case, the whole of chosen new features of mobile objects, which are unconsidered in the present probabilistic estimation, should first be analyzed through a novel neural network. Subsequently, the probabilistic estimation in each one of frames may be made in a better outcome, as long as all the mentioned components are integrated. Evaluation of the proposed approach through PETS-09 database has been finally carried out, once the results with respect to a number of standard benchmark procedures indicate that 12% accuracy improvement is acquired.