Abstract
Detection of moving objects in video streams is the first relevant step of information extraction in many computer vision applications. Aside from the intrinsic usefulness of being able to segment video streams into moving and background components, detecting moving objects provides a focus of attention for recognition, classification, and activity analysis, making these later steps more efficient. This paper implemented a method to detect moving object based on background subtraction. First of all, we establish a reliable background updating model based on statistical and use a dynamic optimization threshold method to obtain a more complete moving object. The moving human bodies are accurately and reliably detected. The experiment results show that the proposed method runs quickly, accurately and fits for the real-time detection.
Highlights
INTRODUCTIONClosed-circuit television (CCTV) is the use of video cameras to transmit a signal to a specific place, on a limited set of monitors
circuit television (CCTV) is a collection of video cameras used for video surveillance
Aside from the intrinsic usefulness of being able to segment video streams into foreground and background components, detecting moving objects provides a focus of attention for recognition, classification, and activity analysis, making these later steps more efficient, since only moving pixels need be considered [2].The usual approach to moving object detection is through background subtraction, that consists in maintaining an up-to date model of the background and detecting moving objects as those that deviate from such a model
Summary
Closed-circuit television (CCTV) is the use of video cameras to transmit a signal to a specific place, on a limited set of monitors. The detection of moving objects in video streams is the first relevant step of information extraction in many computer vision applications. Aside from the intrinsic usefulness of being able to segment video streams into foreground and background components, detecting moving objects provides a focus of attention for recognition, classification, and activity analysis, making these later steps more efficient, since only moving pixels need be considered [2].The usual approach to moving object detection is through background subtraction, that consists in maintaining an up-to date model of the background and detecting moving objects as those that deviate from such a model. Compared to other approaches, such as optical flow [3], this approach is computationally affordable for real-time applications.The background image is not fixed but must adapt to: Illumination changes, sudden (such as clouds) ,Motion changes ,camera oscillations, highfrequencies background objects (such as tree branches, sea waves, and similar) Changes in the background geometry
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