Background subtraction is a method commonly used to segment objects of interest in image sequences. By comparing new frames to a background model, regions of interest can be found. To cope with highly dynamic and complex environments, a mixture of several models has been proposed in the literature. We propose a novel background subtraction technique derived from the popular mixture of Gaussian models technique (MGM). We discard the Gaussian assumptions and use models existing of an average and an upper and lower threshold. Additionally, we include a maximum difference with the previous value and present an intensity allowance to cope with gradual lighting changes and photon noise, respectively. Moreover, edge-based image segmentation is introduced to improve the results of the proposed technique. This combination of temporal and spatial information results in a robust object detection technique that deals with several difficult situations. Experimental analysis shows that our system is more robust than MGM and more recent techniques, resulting in less false positives and negatives. Finally, a comparison of processing speed shows that our system can process frames up to 50% faster.
Detection and segmentation of objects of interest in image sequences is the first major processing step in visual
surveillance applications. The outcome is used for further processing, such as object tracking, interpretation,
and classification of objects and their trajectories. To speed up the algorithms for moving object detection,
many applications use techniques such as frame rate reduction. However, temporal consistency is an important
feature in the analysis of surveillance video, especially for tracking objects. Another technique is the downscaling
of the images before analysis, after which the images are up-sampled to regain the original size. This method,
however, increases the effect of false detections. We propose a different pre-processing step in which we use a
checkerboard-like mask to decide which pixels to process. For each frame the mask is inverted to avoid that
certain pixel positions are never analyzed. In a post-processing step we use spatial interpolation to predict the
detection results for the pixels which were not analyzed. To evaluate our system we have combined it with a
background subtraction technique based on a mixture of Gaussian models. Results show that the models do not
get corrupted by using our mask and we can reduce the processing time with over 45% while achieving similar
detection results as the conventional technique.
This paper gives an introduction to technologies and methodologies to measure performance of MPEG-21 applications in mobile environments. Since resources, such as processing time, available memory, storage, network, and battery time, are very sparse on mobile devices, it is important to optimize technologies to use as little as possible of those resources. To identify possible optimization points for MPEG-21 technologies, performance measurements technologies are applied on a prototype implementation of MPEG-21 Digital Item Declaration and Digital Item Processing. The upcoming MPEG-21 its goal is providing transparent and augmented use of multimedia resources across a plethora of networks and devices. The prototype, which has been implemented on the J2ME platform, gives information about possible bottlenecks when designing MPEG-21 based applications. The results of the measurements are discussed and used to identify which improvements need to be realized to reduce memory and processor consumption when implementing the discussed parts of the MPEG-21 standards on a mobile platform. This paper ends with a discussion and concluding remarks.