Enhancing object detection performance by integrating motion objectness and perceptual organization

In this paper we propose a method to improve the performance of motion detection algorithms by estimating the probability that a detected blob (i.e. a group of pixels identified as foreground) is actually an object of interest. The system exploits “objectness” and perceptual organization to estimate general properties of real-world objects such as convexity, symmetry, well-defined boundary, visual contrast and cohesiveness. The measures of these properties are given as input to a naive Bayes classifier, which is trained to distinguish objects of interest from false positives. The system was trained and tested on two “real-life” environments (underwater and vehicular monitoring) and the results showed an increase of the performance of four state-of-art motion detection algorithms of about 15%. We also tested our approach on the CAVIAR dataset and although the system was not trained on that specific object class (people) it was able to increase the object detection performance of about 10%.