Intelligent monitoring apparatus for real time tracking motion target area

Abstract

The present invention discloses an intelligent monitoring method for the real-time trace of a moving target region, comprising a video input device, a visual analysis system and a network input-output device. In order to track the moving target in high real time and at high precision, the system adopts a target track algorithm which is based on the model dynamic switching, adopts a simple quick model, which is based on the region trace, according to the effective determination of a shielding state, for a single moving target that is not shielded, and adopts a narrow baseline image matching model, which is based on the SIFT feature, for compound motion targets which are mutually shielded. The system has the advantages of simple structure, high real time performance, high precision, and strong expandability, and the advantage that the system has cable Ethernet and wireless GPRS multiple network access facility and effectively realizes the real time trace function for the moving target region.

Description

technical field [0001] The invention belongs to the field of electronic equipment and relates to an intelligent monitoring device for real-time tracking of a moving target area. Background technique [0002] With the rapid growth of video surveillance demand and the increasing scale of surveillance, manual surveillance is far from meeting the surveillance requirements, and the "intelligence" of video surveillance systems has become more and more urgent. [0003] As the front-end "eyes" of the intelligent video surveillance system, the embedded intelligent network camera's image acquisition, analysis and processing, and network communication capabilities play an increasingly important role in the entire surveillance system. Target tracking is widely used in video surveillance, video coding and military fields. How to achieve stable and efficient tracking of targets of interest in real-time video sequences is a very challenging topic in computer vision. Targets in complex occl...

AI Technical Summary

Problems solved by technology

The area-based tracking method performs simple and fast tracking according to the attribute changes of the moving target area (such as the position and size of the target area, etc.). It works well in scenes with few moving targets, but it is difficult to continue when the moving target is occluded. correct tracking of

The tracking method based on the active contour mainly tracks the outline of the target. Compared with the area-based tracking method, the contour expression has the advantage of reducing the computational complexity. The initialization of is also very sensitive, making it difficult to automatically start tracking

The feature-based tracking method mainly uses the global features (such as area, perimeter, etc.) or local features (such as point, line features, etc.) of the target area to track. This method can deal with the tracking problem under partial occlusion. The difficulty is how to determine The unique feature set of the target. If too many features are used, the system efficiency will be reduced and errors will easily occur

The model-based tracking method uses the target model for matching tracking, which can achieve a more stable tracking effect, but this method needs to construct the target model in advance, and the accuracy of motion analysis depends on the accuracy of the geometric model. The geometric model is very difficult, and the tracking process is computationally complex and time-consuming, making it difficult to achieve real-time moving target tracking

[0004] In the document "Distinctive image features from scale-invariant keypoints", David G. Lowe proposed a scale-space-based image local feature description operator that maintains invariance to image scaling, rotation, and even affine transformation—the SIFT operator. The traditional wide-baseline SIFT feature matching algorithm needs to be calculated at each image resolution and scale to meet the invariance of scale scaling and rotation. Its time complexity is very high, and it cannot meet the real-time requirements when directly used in the tracking system.

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