Efficient Online Tracking-by-Detection With Kalman Filter

Abstract

Fast and reliable visual tracking of multiple objects in videos has a promisingly broad area of application in manufacturing, construction, traffic, logistics, etc., especially so in large-scale applications where it is not feasible to attach markers to many objects for traditional marker-enabled tracking methods. This paper presents a new approach, Kalman-intersection-over-union (KIOU) tracker, for multiobject tracking in videos that integrates a Kalman filter with IOU-based track association methods. The performance of the proposed KIOU tracker is quantitatively evaluated with UA-DETRAC, an open realworld multi-object detection and tracking benchmark. Experimental results show that the KIOU tracker outperforms the leading tracking methods. Additionally, the KIOU tracker has speed comparable to simple area overlap-based track association and quality comparable to methods with much higher computational costs, demonstrating its potential for online, real-time multi-object tracking.

Study on Tracking Real-Time Target Human Using Deep Learning for High Accuracy

“…System Architect. In the literature review, to improve the performance of the particle filter for human tracking in complex conditions, the particle filter is combined with other algorithms such as Mean-Shift [26], Kalman filter [7], histogram [27], SURF [28], and so on. For example, regarding figures in a study by Iswanto and Li [29] and Lin et al [30], the common characteristics of these algorithms are facing problems such as feature extraction automatically, real time, scale variation, scene change, similar appearance, cluttered background, and so on.…”

“…However, this algorithm is unstable in illumination, background color, and shape of target human change [6]. The Kalman filter algorithm [7] predicts the position of the target based on previous movement information, but the Kalman filter often is used in a linear system. The particle swarm method with the histogram of oriented gradients (HOG) [8] is the method often used to solve the full occlusion problem.…”

Applied Improved Canny Edge Detection for Diagnosis Medical Images of Human Brain Tumors

“…It can be defined as a type of signal processing using an image for input, while output is either that image or its features. In the case of utilizing such visual tools, the image analysts utilize various interpretive fundamentals [1]. In addition, digital image processing methods provide the ability of computer-assisted alterations of the digital images [2].…”

“…The results from figures(6)(7)(8)(9) & table(1)(2)(3) illustrates the major stages of the suggested approach. Beginning from input the RGB image (brain tumor), convert the color image to gray-level, applying the improved CED and the traditional edge detection methods on gray image (Sobel Filter, Prewitt Filter, Roberts Filter, and Laplacian Filter, traditional CED) one after another for each sample of medical image, and compute the MSE & PSNR Measures the quality of the improved CED.Depending on the measures of PSNR & MSE in Table…”