Detecting Perceptual Color Changes from Sequential Images for Scene Surveillance

This paper proposes a methodology for detecting the appearance of matte-surfaced objects on a scene using color information and spatial thresholding. First, a difference image is obtained via a pixel-wise comparison of the color content of a 'clean' reference image and a sample image. Then, spatial thresholding of the difference image is performed to extract any objects of interest, followed by morphological post-processing to remove pixel noise. We study the applicability of two alternate color spaces (HSV, CIE Lab) for computing the difference image, and similarly, two alternate spatial thresholding methods based on Euler numbers and stochastic Poisson model are evaluated. We demonstrate the performance of the proposed approach in scene surveillance, where the objective is to monitor a shipping dock for the appearance of needless objects such as cardboard boxes. In order to analyze the robustness of the approach, the experiment includes three different types of scenes categorized as 'easy', 'moderate', and 'difficult', based on scene properties such as the heterogeneity of the background and the existence of illumination changes and shadows. The experimental results show that relatively good recognition accuracy is achieved on 'easy' and 'moderate' scenes, whereas 'difficult' scenes remain a challenge for future work.