Smoothing of optical flow using robustified diffusion kernels

Parallelization of the optical flow computation in sequences from moving cameras

“…Over the years, a lot of research has been carried out in the field of optical flow algorithms and the latter have been continuously improved, sometimes by concentrating on the algorithm itself [19,[29][30][31], sometimes by combining two of them [32,33], and sometimes by combining with other techniques [4,16,34]. Although most optical flow algorithms were designed with the main objective of obtaining accurate results, the trade-offs between efficiency and accuracy in optical flow algorithms are highlighted in [35] as well as the importance of an efficient optical flow computation in many real-world applications.…”

Adaptive Threshold for Background Subtraction in Moving Object Detection using Stationary Wavelet Transforms 2D

“…While detecting and tracking, we need to analyze video sequences to detect and track target in each frame, to achieve monitoring and to master the dynamic variation of the moving objects in order to confirm their exact position. In general, there are lots of methods which can be classified into three categories: optical flow [15,37], [16], temporal difference [13], [14] and background subtraction. The algorithms of temporal difference quickly adapt to sudden changes in the environment, but the resulting shapes of target are frequently incomplete.…”

“…For us, our contribution consists to integrated this technique in video sequences to detect moving objects with stationary wavelet transforms 2D. According to the difficulty of motion detection in video surveillance, the most used techniques deal with a fixed camera [15,16] or closed world representations [17] which rely on a fixed background or a specific knowledge on the type of actions taking place, where various difficult cases are not perfectly www.ijacsa.thesai.org solved and must be improved such as identification, occlusion, tracking of object, localization and removing shadows of objects.…”

A self-adaptive optical flow method for the moving object detection in the video sequences

“…Different from it, an adaptive background generation method is presented by [11], and needs to calculate the object distance with a special function. Optical flow algorithm [12][13][14] can acquire object direction and velocity directly. A new optical flow smoothing methodology combining vector diffusion and robust statistics is proposed by [12], and an efficient statistical method for subpixel optical flow detection is described in [13], which is based on the randomized sampling and voting process, however, there exist some limitations when it is applied to the detection of slow moving objects.…”

“…Optical flow algorithm [12][13][14] can acquire object direction and velocity directly. A new optical flow smoothing methodology combining vector diffusion and robust statistics is proposed by [12], and an efficient statistical method for subpixel optical flow detection is described in [13], which is based on the randomized sampling and voting process, however, there exist some limitations when it is applied to the detection of slow moving objects. In addition, two illumination-robust variational methods are developed in [14], and are all based on the cross-correlation detection method.…”