Monitoring small objects against cluttered moving backgrounds is a huge challenge to future robotic vision systems. As a source of inspiration, insects are quite apt at searching for mates and tracking prey, which always appear as small dim speckles in the visual field. The exquisite sensitivity of insects for small target motion, as revealed recently, is coming from a class of specific neurons called small target motion detectors (STMDs). Although a few STMD-based models have been proposed, these existing models only use motion information for small target detection and cannot discriminate small targets from small-target-like background features (named fake features). To address this problem, this paper proposes a novel visual system model (STMD+) for small target motion detection, which is composed of four subsystems--ommatidia, motion pathway, contrast pathway, and mushroom body. Compared with the existing STMD-based models, the additional contrast pathway extracts directional contrast from luminance signals to eliminate false positive background motion. The directional contrast and the extracted motion information by the motion pathway are integrated into the mushroom body for small target discrimination. Extensive experiments showed the significant and consistent improvements of the proposed visual system model over the existing STMD-based models against fake features.
Traditional motion detection methods such as optical flow @cite_32 , background subtraction @cite_8 and temporal differencing @cite_26 , have been developed to detect normal-sized objects like pedestrians and vehicles. They utilize physical characteristics including shape, color and texture, to segment regions corresponding to moving objects from the background. Nonetheless, these methods would be powerless for objects that are as small as one pixel or a few pixels, because it is difficult to identify objects' physical characteristics in such small sizes. Additionally, the above-mentioned methods may not work for cluttered moving backgrounds, as small moving objects could be submerged among the pixel error when applying background motion compensation @cite_13 .
@cite_26 @cite_13 @cite_32 @cite_8
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This paper proposes a new background subtraction method for detecting moving foreground objects from a nonstationary background. While background subtraction has traditionally worked well for a stationary background, the same cannot be implied for a nonstationary viewing sensor. To a limited extent, motion compensation for the nonstationary background can be applied. However, in practice, it is difficult to realize the motion compensation to sufficient pixel accuracy, and the traditional background subtraction algorithm will fail for a moving scene. The problem is further complicated when the moving target to be detected tracked is small, since the pixel error in motion that is compensating the background will subsume the small target. A spatial distribution of Gaussians (SDG) model is proposed to deal with moving object detection having motion compensation that is only approximately extracted. The distribution of each background pixel is temporally and spatially modeled. Based on this statistical model, a pixel in the current flame is then classified as belonging to the foreground or background. For this system to perform under lighting and environmental changes over an extended period of time, the background distribution must be updated with each incoming frame. A new background restoration and adaptation algorithm is developed for the nonstationary background. Test cases involving the detection of small moving objects within a highly textured background and with a pantilt tracking system are demonstrated successfully. A new background subtraction method is proposed in this paper for the foreground detection from a non-stationary background. Usually, motion compensation is required when applying background subtraction to a non-stationary background. In practice, it is difficult to realize this to sufficient pixel accuracy. The problem is further complicated when the moving objects to be detected tracked are small, since the pixel error in motion compensating the background will hide the small targets. A spatial distribution of Gaussians model is proposed to deal with moving object detection where the motion compensation is not exact but approximated. The distribution of each background pixel is temporally and spatially modeled. Based on this statistical model, a pixel in the current frame is classified as belonging to the foreground or background. In addition, a new background restoration and adaptation algorithm is developed for the non-stationary background over an extended period of time. Test cases involving a surveillance system to detect small moving objects (human and car) within a highly textured background and a pan-tilt human tracking system are demonstrated successfully. This paper presents a technique for motion detection that incorporates several innovative mechanisms. For example, our proposed technique stores, for each pixel, a set of values taken in the past at the same location or in the neighborhood. It then compares this set to the current pixel value in order to determine whether that pixel belongs to the background, and adapts the model by choosing randomly which values to substitute from the background model. This approach differs from those based upon the classical belief that the oldest values should be replaced first. Finally, when the pixel is found to be part of the background, its value is propagated into the background model of a neighboring pixel. We describe our method in full details (including pseudo-code and the parameter values used) and compare it to other background subtraction techniques. Efficiency figures show that our method outperforms recent and proven state-of-the-art methods in terms of both computation speed and detection rate. We also analyze the performance of a downscaled version of our algorithm to the absolute minimum of one comparison and one byte of memory per pixel. It appears that even such a simplified version of our algorithm performs better than mainstream techniques. Motion detection algorithms that can be applied to surveillance cameras such as CCTV (Closed Circuit Television) have been studied extensively. Motion detection algorithm is mostly based on background subtraction. One main issue in this technique is that false positives of dynamic backgrounds such as wind shaking trees and flowing rivers might occur. In this paper, we proposed a method to search for dynamic background region by analyzing the video and removing false positives by re-checking false positives. The proposed method was evaluated based on CDnet 2012 2014 dataset obtained at "changedetection.net" site. We also compared its processing speed with other algorithms.