Moving object detection using modified temporal differencing and local fuzzy thresholding

Most of the existing video object detection schemes are either computationally extensive or fail to detect moving objects in different challenging situations. In this paper, we propose a robust and computationally inexpensive scheme to detect moving objects in video. The threefold approach begins with computation of difference images using temporal information. Difference images are calculated by subtracting two input frames, at each pixel position. Instead of generating difference images using the traditional continuous frame difference approach, we propose using a fixed number of alternate frames centered around the current frame. This approach aids in reducing the computational complexity without compromising on quality of the difference images. After computation of difference images, a novel post-processing scheme is employed by utilizing gamma correction factor and Mahalanobis distance metric to reduce false positives and false negatives. Object segmentation is finally performed on the refined difference image by a local fuzzy thresholding scheme. This avoids problems that are usually encountered in hard thresholding, especially pixel misclassification, which is the most important one. For robust experimental analysis, videos from changedetction.net, CAVIAR, and http://perception.i2r datasets have been used. These selected videos contain a wide variety of common challenges faced during object detection. Some examples are the presence of dynamic backgrounds, shadows, bad weather, etc. The results establish the effectiveness of the proposed scheme over some of the existing schemes both qualitatively and quantitatively as delineated in the experimental result section.