Target detection system and method based on self-adaption combined wave filtering and multilevel detection

Abstract

The invention relates to a target detection system and method based on self-adaption combined wave filtering and multilevel detection. The system includes a target detection module combing a moving target detection unit with an obvious target detection unit, a target identification module based on a convolutional neural network and a target tracking module based on combination decision and multi-channel image features; the target detection module, the target identification module and the target tracking module tightly cooperate with one another to constitute the stable and reliable target detection system together. System output includes detected candidate target position information, target classification information and position information of selected targets obtained by target tracking. The target detection system is achieved on a high-performance multi-core DSP chip and used for carrying out targeted optimization on the multi-core DSP chip, real-time target detection and target tracking are achieved, and a function of quickly identifying a target is achieved. The target detection system and method have the advantages of being high in practicability and feasibility and convenient to integrate into various solution schemes with target detection demands, and can achieve intelligent target detection, identification and tracking.

Description

technical field [0001] The present invention relates to the field of pattern recognition and machine learning, in particular to a target detection system and method based on adaptive combined filtering and multi-level detection. Background technique [0002] When human beings use vision to observe the scene, they will first find some interesting objects in the scene, and then judge their categories through perception, and then continue to observe the most interesting objects. In the field of pattern recognition and machine learning, in order to imitate human behavior to realize computer vision, these tasks are abstracted into target detection, target recognition and target tracking. [0003] Target detection is the first step in target detection, that is, to detect targets that need further processing from the scene. Commonly used target detection methods include inter-frame difference, background modeling and methods based on deep neural networks. Among them, the inter-fra...

AI Technical Summary

Problems solved by technology

The features with scale and rotation invariance can finely represent the local area in the image, but the information contained in other areas in the image has not been well utilized

The histogram of gradient statistics adopts a dense representation, which can accurately extract the gradient details of the image, but cannot distinguish images with similar textures well

Color features can distinguish images of different colors, but cannot represent the shape information of images well

The representation forms of the above features are quite different. In the existing methods, only one or two of them can be used at the same time, and the features are not organically combined, so that the algorithm cannot exert the maximum performance of the comprehensive features, and the obtained target tracking method is stable. Not high enough, the target tracking result is not accurate enough

And there are many challenging scenarios in object tracking, the existing method of object tracking through a single tracker is prone to tracking failure during operation

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