Abstract—Motion detection, the process which segments moving objects in video streams, is the first critical process and plays an important role in video surveillance systems. Dynamic scenes are commonly encountered in both indoor and outdoor situations and contain objects such as swaying trees, spouting fountains, rippling water, moving curtains, and so on. However, complete and accurate motion detection in dynamic scenes is often a challenging task. This paper presents a novel motion detection approach based on radial basis function artificial neural networks to accurately detect moving objects not only in dynamic scenes but also in static scenes. The proposed method involves two important modules: a multibackground generation module and a moving object detection module. The multibackground generation module effectively generates a flexible probabilistic model through an unsupervised learning process to fulfill the property of either dynamic background or static background. Next, the moving object detection module achieves complete and accurate detection of moving objects by only processing blocks that are highly likely to contain moving objects. This is accomplished by two procedures: the block alarm procedure and the object extraction procedure. The detection results of our method were evaluated by qualitative and quantitative comparisons with other state-of-the-art methods based on a wide range of natural video sequences. The overall results show that the proposed method substantially outperforms existing methods with Similarity and F1 accuracy rates of 69.37% and 65.50%, respectively
Abstract—Attention is an integral part of the human visual system and has been widely studied in the visual attention literature. The human eyes fixate at important locations in the scene, and every fixation point lies inside a particular region of arbitrary shape and size, which can either be an entire object or a part of it. Using that fixation point as an identification marker on the object, we propose a method to segment the object of interest by finding the “optimal” closed contour around the fixation point in the polar space, avoiding the perennial problem of scale in the Cartesian space. The proposed segmentation process is carried out in two separate steps: First, all visual cues are combined to generate the probabilistic boundary edge map of the scene; second, in this edge map, the “optimal” closed contour around a given fixation point is found. Having two separate steps also makes it possible to establish a simple feedback between the mid-level cue (regions) and the low-level visual cues (edges).
– This paper presents a no-reference video quality metric that blindly estimates the quality of a video. The proposed system is based on video watermarking using 8x8 blocks DCT coefficients of YCBCR domain, and for watermark generation; the Geffe generator has been used to generate binary stream sequence watermark in embedding and extracting processor. Data hiding is achieved by simple “even-odd” signaling of the DCT coefficients. The comparison process between the extracted watermark and the generated watermark from Geffe generator was calculated to conclude the video quality assessment by measuring the watermark degradation. An identical watermark within each frame has been used in this system. With these mechanisms, the proposed method is robust against the attacks of frame dropping, averaging, swapping, and statistical analysis. The results indicate that the proposed video quality metric outperforms standard Peak Signal to Noise Ratio (PSNR) and structural similarity and Image Quality (SSIM) metric in estimating the perceived quality of a video.