基于时域和梯度的运动目标识别跟踪算法

Moving Object Recognition and Tracking Algorithm Based on Time Domain and Gradient

针对视频存在噪声干扰及现有算法实时性较差的问题,提出了一种新型的基于时域和梯度的运动目标识别跟踪算法.考虑时间域上视频的形成,用帧差法将图片中的区域划分为变化区域和运动区域;依照2个区域的幅度变化对识别算法进行设计,为了消除背景干扰,对形成的运动区域进行均值滤波;在时域基础上增加了梯度分量,对运动区域形成梯度化轨迹,实现跟踪算法的完整设计.通过实验验证,本算法能够稳定地跟踪前景运动目标,与TLD和CamShift算法相比,在实时性上有明显提升,在耗时上分别缩减12.6%和22.7%.

Aiming at the problem of noise interference in video and the poor real-time performance of existing algorithms,a new moving object recognition and tracking algorithm based on time domain and gradient is proposed.Considering the formation of video in time domain,the region in the picture is divided into changing region and moving region by frame difference method;the recognition algorithm is designed according to the magnitude change of 2 regions,and the formed moving area is filtered with mean in order to eliminate background interference;the gradient component is added on the basis of the time domain,the gradient trajectory is formed for the moving area,and the complete design of the tracking algorithm is realized.Experiments show that the proposed algorithm can track foreground moving targets stably.Compared with TLD and Camshift algorithms,the proposed algorithm can reduce the time consumption by 12.6%and 22.7%,respectively.