**6. Conclusions**

The detection and tracking of objects in a sequence of images or video is a topical need for several applications such as video conferencing, video indexing and especially video surveillance. Computer vision with a Human Interface Machine "HIM" is therefore an issue actively studied in many domains, especially since the prices of acquisition and processing equipment have become more attractive. This is an area that touches on everything, starting from the problems of acquisition with different linked effects and where the originality of simple ideas can still bring a lot. In this chapter, we introduced the Kalman filter algorithm for tracking and detection objects and multi-objects. Localization, target tracking, and detection objects were provided as examples for reader's better understanding of practical usage of the Kalman filters. We proceeded to the implementation of the different modules of object tracking algorithm through the estimation of calculation parameters using a Kalman filter. The results obtained make it possible to meet the monitoring requirements of several video surveillance applications. On the one hand, the localization precision achieved by our system makes it a standard module for detection or identification or object tracking systems. On the other hand, a flow at a frequency of 20 frames per second was considered, which is reasonable for an object tracking system with a minimum execution time. The tracking algorithm with its different modules must be tested with other video sequences. Although the implementation of monitoring systems has certain weaknesses, our method has given promising results. Many avenues can be envisaged to continue this work. First of all, note that we tested the algorithm implemented for tracking two objects (a car and a pedestrian in the sequence of "PETS 2001 (1)" and two cars in the sequence of "PETS 2001 (2)"), and it can be applied for tracking multiple objects in a video sequence. Then, use the detection algorithm based on Adaboost classifiers upstream of the tracking algorithm (Camshift and Kalman filter). The association of these two modules is based on a cascade of Adaboost classifiers, improves the calculation time and improves the quality of tracking of one or more objects in a sequence of images or video. Then, validation of the detection and tracking system for faces and other objects (pedestrians, cars, hand gestures, glass, etc.) on an FPGA target platform (Saber-Lite with ARM-Cortex-A9MP). Our solution optimize the time of execution and other criteria in frame and video processing. In future, we intend to extensively evaluate the method quantitatively so that it can be well tested before trying on computer vision practice.

**Author details**

Systems (LT2S), Sfax, Tunisia

2 ENET'COM, University of Sfax, Sfax, Tunisia

provided the original work is properly cited.

\*, Fahmi Ghozzi1,2 and Ahmed Fakhfakh1,2

*Estimation for Motion in Tracking and Detection Objects with Kalman Filter*

*DOI: http://dx.doi.org/10.5772/intechopen.92863*

1 Digital Research Center of SFAX (CRNS), Laboratory of Technology for Smart

\*Address all correspondence to: salhiafefge@gmail.com; afef.salhi.ge@enis.tn

© 2020 The Author(s). Licensee IntechOpen. This chapter is distributed under the terms of the Creative Commons Attribution License (http://creativecommons.org/licenses/ by/3.0), which permits unrestricted use, distribution, and reproduction in any medium,

Afef Salhi<sup>1</sup>

**59**

*Estimation for Motion in Tracking and Detection Objects with Kalman Filter DOI: http://dx.doi.org/10.5772/intechopen.92863*
