Multi-target tracking algorithm by corner feature for video stream

In the field of computer vision research, moving target tracking of sequence images is not well solved, and is an old, important and challenging task. Fast and accurate completion of moving target tracking is very an important and difficult problem. In a dynamic environment, moving target tracking is often influenced by various factors, such as weather changes, shadows, occlusion, background confusion. This paper presents a multi-target tracking algorithm by corner feature for video stream, which can solve the problems of video multi-objective motion tracking. The improved Harris operator can extract some even and stable corners, and the matching optimization reduces the amount of false matching points, so they improve the accuracy of target tracking. Experimental results show that the algorithm can complete a stable matching under a variety of complex conditions, and achieve a steady target tracking under a small part of the block state. Introduction The technology of moving target tracking as one of the core technologies in the field of computer vision has an increasingly important role in a number of key technologies, such as virtual reality, robotics, intelligent weapon systems [1-4]. Target tracking is obtained by motion parameters of moving objects, such as position, velocity, acceleration and trajectory, etc., for further processing, analysis, and understanding the behavior of moving objects in order to complete higher level tasks. In recent years, the problems of target tracking have been the climax of the study. Ref. [5] divides the target tracking problem into many types, such as single-camera visual tracking and multi-camera visual tracking, rigid tracking and non-rigid tracking, target tracking when the camera is at rest and target tracking when the camera is moving, etc. The practical methods of target tracking are mainly based on the area, contour, centroid, correlation, feature recognition. Feature-based tracking method is the research focus of the current tracking method. Corner information in the target feature is a comprehensive description of the target, which extraction and tracking of the corners reflect static characteristics and dynamic characteristics of the target. Corners generally are understood to be the larger curvature points of the object edge in an image, and they have two key characteristics, which are located in the larger curvature points and the points on the edge of the object. Corner tracking has two main key: corner extraction and corner matching. Corner extraction in general is mainly based on the edge and the grayscale, and corner matching is based on relevant corner template and corner feature. Experimental results show that target tracking using a certain amount of corners is effective in general. But too many corners will increase computation time, and the accuracy and reliability of corner extraction will affect corner matching in the multi-frame images. In order to accurately extract the corners and reduce false matching corners, and improve operational efficiency, and also achieve effectively target tracking, motion analysis and recognition, we use the improved Harris operator [6] to extract feature points of moving target, and complete the tracking of multiple moving targets by feature matching and matching optimization in the video images. Moving target region detection and multi-target labeling The region detection algorithm of moving target includes three steps, such as initial background extraction, motion region extraction, background updating, as follows: (1) Initial background extraction Select the initial five frame of a video sequence, and use the background modeling algorithm International Conference on Advances in Mechanical Engineering and Industrial Informatics (AMEII 2015) © 2015. The authors Published by Atlantis Press 719 based on the mean to extract the initial background of video image sequence. (2) Motion region extraction Use the background subtraction algorithm [7] to extract the moving object, which is that the current frame and the extracted background make the differential. The operation can be described as:    > − = others T y x B t y x f if t y x fb , 0 ) 0 , , ( ) , , ( , 1 ) , , ( (1) where T is the threshold value, ( , , ) f x y t and ) 0 , , ( y x B are respectively the current frame at t time and the background image. (3) Background Updating To reduce the amount of computation and save memory, you can update the background by the following equation: ) , , ( ) 1 ( ) , , ( ) 1 , , ( t y x B t y x f t y x B λ λ − + = + (2) where ( , , ) f x y t and ) , , ( t y x B are respectively the current frame and its background image at t time, λ is called refresh rate, usually taken 0.05. The most common algorithms of the connected components are the recursive algorithm and the sequential algorithm. The recursive algorithm is inefficient, especially for multiple targets in the binary image, and you need to repeatedly scan the image. The sequential algorithm usually only scans the image twice, and its processing speed is much faster than the recursive algorithm. So the sequential algorithm usually is used to complete the labeling, the process is as follows: (1) An image is scanned from left to right, top to bottom. (2) If the pixel is "1", those pixels located in its eight neighborhood are given the same reference numerals, and then the "l" pixels in the eight neighborhood are denoted by the same reference numerals, otherwise they are assigned new tags and the tags are input the equivalent table. (3) If there are not the scanned pixels in the image, it returns step 1. (4) Find the minimum tag in each equivalent collection of the equivalent table. (5) Scan the whole image, and substitute the minimum tag in the equivalent table for each tag. Improved Harris corner algorithm In this paper, the improved Harris operator extracts some corners, so as to obtain sub-pixel accuracy. Harris corner operator makes corner extraction, as follows: