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The detection of moving objects is a trivial task performed by vertebrate retinas, yet a complex computer vision task. Object-motion-sensitive ganglion cells (OMS-GC) are specialised cells in the retina that sense moving objects. OMS-GC take as input continuous signals and produce spike patterns as output, that are transmitted to the Visual Cortex via the optic nerve. The Hybrid Sensitive Motion Detector (HSMD) algorithm proposed in this work enhances the GSOC dynamic background subtraction (DBS) algorithm with a customised 3-layer spiking neural network (SNN) that outputs spiking responses akin to the OMS-GC. The algorithm was compared against existing background subtraction (BS) approaches, available on the OpenCV library, specifically on the 2012 change detection (CDnet2012) and the 2014 change detection (CDnet2014) benchmark datasets. The results show that the HSMD was ranked overall first among the competing approaches and has performed better than all the other algorithms on four of the categories across all the eight test metrics. Furthermore, the HSMD proposed in this paper is the first to use an SNN to enhance an existing state of the art DBS (GSOC) algorithm and the results demonstrate that the SNN provides near real-time performance in realistic applications.

Increasing the demand for object motion detection (OMD) requires shifts of reducing redundancy, heightened power efficiency, and precise programming capabilities to ensure consistency and accuracy. Drawing inspiration from object motion-sensitive ganglion cells, we propose an OMD vision sensor with a simple device structure of a WSe 2 homojunction modulated by a ferroelectric copolymer. Under optical mode and intermediate ferroelectric modulation, the vision sensor can generate progressive and bidirectional photocurrents with discrete multistates under zero power consumption. This design enables reconfigurable devices to emulate long-term potentiation and depression for synaptic weights updating, which exhibit 82 states (more than 6 bits) with a uniform step of 6 pA. Such OMD devices also demonstrate nonvolatility, reversibility, symmetry, and ultrahigh linearity, achieving a fitted R 2 of 0.999 and nonlinearity values of 0.01/-0.01. Thus, a vision sensor could implement motion detection by sensing only dynamic information based on the brightness difference between frames, while eliminating redundant data from static scenes. Additionally, the neural network utilizing a linear result can recognize the essential moving information with a high recognition accuracy of 96.8%. We also present the scalable potential via a uniform 3 × 3 neuromorphic vision sensor array. Our work offers a platform to achieve motion detection based on controllable and energy-efficient ferroelectric programmability.

Moving object detection task can be solved by the background subtraction algorithm if the camera is fixed. However, because the background moves, detecting moving objects in a moving car is a difficult problem. There were attempts to detect moving objects using LiDAR or stereo cameras, but when the car moved, the detection rate decreased. We propose a moving object detection algorithm using an object motion reflection model of motion vectors. The proposed method first obtains the disparity map by searching the corresponding region between stereo images. Then, we estimate road by applying v-disparity method to the disparity map. The optical flow is used to acquire the motion vectors of symmetric pixels between adjacent frames where the road has been removed. We designed a probability model of how much the local motion is reflected in the motion vector to determine if the object is moving. We have experimented with the proposed method on two datasets, and confirmed that the proposed method detects moving objects with higher accuracy than other methods.

Moving object detection task can be solved by the background subtraction algorithm if the camera is fixed. However, because the background moves, detecting moving objects in a moving car is a difficult problem. There were attempts to detect moving objects using LiDAR or stereo cameras, but when the car moved, the detection rate decreased. We propose a moving object detection algorithm using an object motion reflection model of motion vectors. The proposed method first obtains the disparity map by searching the corresponding region between stereo images. Then, we estimate road by applying v-disparity method to the disparity map. The optical flow is used to acquire the motion vectors of symmetric pixels between adjacent frames where the road has been removed. We designed a probability model of how much the local motion is reflected in the motion vector to determine if the object is moving. We have experimented with the proposed method on two datasets, and confirmed that the proposed method detects moving objects with higher accuracy than other methods. [ABSTRACT FROM AUTHOR]