In the computer vision field, moving object detection is an important part. The overall operations of tracking and identification of parts afterwards depend on a robust and precise detection outcome that makes the moving object detection step significantly. In several scenarios, the moving object detection is highly useful. These scenarios are motion analysis, intelligent video surveillance, and human-machine interface. A surveillance system can be determined as a technological tool, which assists human by delivering a reasoning capability and extended perception about the situations of interest, which arise in the monitored environment. human reasoning and perception are constrained by the limits and capabilities of the human mind and senses in order to simultaneously gather, store and process limited data. The visual surveillance primarily focused on intelligent visual surveillance (IVS) in a wide range area. The various research formulated in IVS can be separated widely into wide area surveillance and image interception control techniques. Primarily, the aim of the image interception techniques is to extract the high-level information of visual motion detection, tracking, object recognition, and behaviour understanding. The function of the moving object detecting system is to identify and detect the moving object from a complex outdoor. A high definition camera is used on a High-precision Intelligent holder. Further, an accuracy of the position of a presetting point is 0.1 degree. For the multiple of accessing, the reliable repetitive precision is available. The detecting system is mainly designed for continuous operation. Minimum speed 0.01 degree per second and maximum speed 25 degrees per second satisfy various requirements of short and long-range observation. The data echo od holder angle and focal length of the lens are supported that is convenient to the backend to process the intelligent analysis. A stabilized gyro connector is offered at the holder itself, which together with the gyro instrument, will make sure that the image must be stable on-vehicle, on-plane, on-ship conditions. When the holder revolves around in a provided rule, the camera can get a complete image of these areas. There are some widely and traditionally used algorithms that include traditional background subtraction method, frame difference method, statistical learning method and optical flow method. however, the first and second methods work only on a stationary background. However, the dynamic background video is gained by the camera. Further, the first two methods are cannot be used in this formulation. The statistical learning method has a huge amount of calculation. Where the method of edge detection is not costly in terms of computation time. Additionally, the Local Binary Pattern (LBP) algorithm has less computational complexity and do not have any complex interactive process.
What is intelligent visual surveillance?
The conventional passive surveillance system is improved by the intelligent visual surveillance through the tracking and automated object recognition, retrieval or indexing of visual events and scene interpretation. Further, the visual surveillance techniques have initiated within a large variety of applications in access control, alarming in academic, anomaly detection and person-specific identification community and government as well as industry. The successful system for the visualization techniques are the Smart Surveillance System of IBM, Visual Surveillance and Monitoring (VSAM), and the Annotated digital video for surveillance and optimized retrieval (ADVISOR) are developed by combining system engineering, computer vision, and communication techniques. It is found that intelligent multi-camera video surveillance can emphasize the various integration and connection of different modules.
What is moving object detection?
The background modelling and moving object detection always deal with the five major challenges such as video noise, bootstrapping, camouflage, dynamic background, and illumination changes. Maximum background modelling functions begin around these challenges. There is a technique named hybrid background subtraction technique, which can be used to deal with bootstrapping problems. There is also a motion detection method based on the radial basis function artificial neural networks in order to appropriately detect the moving objects not only the within the dynamic scenes or background, but also in the static scenes or background. Further, an approach is there which can be applied in both the video stream such as in high bit rate video stream and in low bit rate video stream as well. A unified framework is also out there to address the difficulties of irregular object movement and dynamic background, especially the difficulties caused by the irregular object movement is more than the difficulties caused by the dynamic movement. There is many other new strategies to detect camouflaged moving objects that can further identify accurate camouflaged areas. All the approaches mentioned above solved some problems from the mentioned associated challenges. There is an algorithm named Multi-Block Temporal- analyzing local binary pattern (MB-LBP), which can effectively solve three among the five mentioned challenges and these are video noise, dynamic background, and illumination changes at the same time.