Publication

A probabilistic integrated object recognition and tracking framework

Journal Article (2012)

Journal

Expert Systems with Applications

Pages

7302-7318

Volume

39

Number

8

Doc link

http://dx.doi.org/10.1016/j.eswa.2012.01.088

File

Download the digital copy of the doc pdf document

Abstract

This paper describes a probabilistic integrated object recognition and tracking framework called PIORT, together with two specific methods derived from it, which are evaluated experimentally in several test video sequences. The first step in the proposed framework is a static recognition module that provides class probabilities for each pixel of the image from a set of local features. These probabilities are updated dynamically and supplied to a tracking decision module capable of handling full and partial occlusions. The two specific methods presented use RGB color features and differ in the classifier implemented: one is a Bayesian method based on maximum likelihood and the other one is based on a neural network. The experimental results obtained have shown that, on one hand, the neural net based approach performs similarly and sometimes better than the Bayesian approach when they are integrated within the tracking framework. And on the other hand, our PIORT methods have achieved better results when compared to other published tracking methods in video sequences taken with a moving camera and including full and partial occlusions of the tracked object.

Categories

computer vision, object detection, object recognition, pattern recognition.

Author keywords

object tracking, object recognition, occlusion, performance evaluation, probabilistic methods, video sequences, dynamic environments

Scientific reference

F. Serratosa i Casanelles, R. Alquézar Mancho and N. Amézquita. A probabilistic integrated object recognition and tracking framework. Expert Systems with Applications, 39(8): 7302-7318, 2012.