Moving targets detection and tracking under dynamic background based on multicore TMS320C6678

2015 ◽  
Author(s):  
Shanshan Ge Shanshan Ge ◽  
Baojun Zhao Baojun Zhao ◽  
Shuigen Wang Shuigen Wang ◽  
Meiping Ji Meiping Ji
Keyword(s):  
Moving Targets ◽  
Dynamic Background ◽  
Detection And Tracking

scholarly journals MULTI OBJECT DETECTION AND TRACKING USING OPTICAL FLOW DENSITY – HUNGARIAN KALMAN FILTER (OFD - HKF) ALGORITHM FOR VEHICLE COUNTING

2018 ◽  
Vol 11 (1) ◽  
pp. 17 ◽  
Author(s):  
Muhamad Soleh ◽  
Grafika Jati ◽  
Muhammad Hafizhuddin Hilman
Keyword(s):  
Kalman Filter ◽  
Object Detection ◽  
Optical Flow ◽  
Motion Blur ◽  
Research Topic ◽  
Vehicle Tracking ◽  
Flow Density ◽  
Illumination Change ◽  
Dynamic Background ◽  
Detection And Tracking

Intelligent Transportation Systems (ITS) is one of the most developing research topic along with growing advance technology and digital information. The benefits of research topic on ITS are to address some problems related to traffic conditions. Vehicle detection and tracking is one of the main step to realize the benefits of ITS. There are several problems related to vehicles detection and tracking. The appearance of shadow, illumination change, challenging weather, motion blur and dynamic background such a big challenges issue in vehicles detection and tracking. Vehicles detection in this paper using the Optical Flow Density algorithm by utilizing the gradient of object displacement on video frames. Gradient image feature and HSV color space on Optical flow density guarantee the object detection in illumination change and challenging weather for more robust accuracy. Hungarian Kalman filter algorithm used for vehicle tracking. Vehicle tracking used to solve miss detection problems caused by motion blur and dynamic background. Hungarian kalman filter combine the recursive state estimation and optimal solution assignment. The future positon estimation makes the vehicles detected although miss detection occurance on vehicles. Vehicles counting used single line counting after the vehicles pass that line. The average accuracy for each process of vehicles detection, tracking, and counting were 93.6%, 88.2% and 88.2% respectively.

An Industrial Production Line Dynamic Target Tracking System Based on HAAR and CAMSHIFT

2020 ◽  
Vol 34 (11) ◽  
pp. 2059037
Author(s):  
Xuejun Tian ◽  
Haowen Feng ◽  
Jieyan Chen
Keyword(s):  
Target Tracking ◽  
Tracking System ◽  
Raspberry Pi ◽  
Automation System ◽  
Moving Targets ◽  
Embedded Platform ◽  
System A ◽  
Detection And Tracking ◽  
Camshift Algorithm ◽  
Dynamic Target

Aiming at the detection and tracking of moving targets in industrial automation system, a dynamic target tracking algorithm based on HAAR and CAMSHIFT is proposed. A cascade HAAR classifier is designed and trained for tracking targets. CAMSHIFT algorithm is used to track and detect moving targets quickly. The system is tested on Raspberry Pi embedded platform. The results show that the algorithm can detect the target correctly and track the target effectively.

Comparative Study of Moving Target Analysis System

2014 ◽  
Vol 989-994 ◽  
pp. 3122-3126
Author(s):  
Min Feng ◽  
Huai Chang Du
Keyword(s):  
Object Tracking ◽  
Tracking System ◽  
Moving Target ◽  
Motion Trajectory ◽  
Moving Targets ◽  
Image System ◽  
Target Analysis ◽  
Detection And Tracking ◽  
Analysis System ◽  
Experiment Analysis

This paper compares two kinds of moving target analysis systems, which are the motion history image system and the moving object tracking system. Each system includes two parts which are moving target detection and tracking, achieving respectively detection of the direction of moving targets or representation of motion trajectory. Through experiment analysis of moving human and vehicles, each system is determined which situation it is suitable for.

Registration, detection, and tracking of moving targets in rotating barscan imagery

2006 ◽  
Author(s):  
Hassan Beydoun ◽  
Arthur Forman ◽  
Jamie C. Perez ◽  
Abhijit Mahalanobis
Keyword(s):  
Moving Targets ◽  
Detection And Tracking

scholarly journals Moving-object detection and tracking by scanning LiDAR mounted on motorcycle based on dynamic background subtraction

2021 ◽  
Author(s):  
Shotaro Muro ◽  
Ibuki Yoshida ◽  
Masafumi Hashimoto ◽  
Kazuhiko Takahashi
Keyword(s):  
Object Detection ◽  
Moving Object Detection ◽  
Moving Object ◽  
Global Navigation Satellite Systems ◽  
Measurement Unit ◽  
Dynamic Background ◽  
Object Detection And Tracking ◽  
Detection And Tracking ◽  
Scanning Lidar ◽  
Scan Data

AbstractThis paper presents a method for moving-object detection and tracking (DATMO) in global navigation satellite systems (GNSS)-denied environments using a light detection and ranging (LiDAR) mounted on a motorcycle. Distortion in the scanning LiDAR data is corrected by estimating the pose (3D positions and attitude angles) of the motorcycle in a period shorter than the LiDAR scan period using normal distributions transform-based simultaneous localization and mapping (NDT-based SLAM) and the information from an inertial measurement unit (IMU) via the extended Kalman filter (EKF). The scan data of interest are extracted by subtracting the local environment map generated by NDT-based SLAM from the LiDAR scan data. Moving objects are detected from the scan data of interest using an occupancy grid method and are tracked with a Bayesian filter. Experimental results obtained from public road and university campus environments demonstrate the effectiveness of the proposed method.

scholarly journals Location Detection and Tracking of Moving Targets by a 2D IR-UWB Radar System

Sensors ◽  
2015 ◽  
Vol 15 (3) ◽  
pp. 6740-6762 ◽  
Author(s):  
Van-Han Nguyen ◽  
Jae-Young Pyun
Keyword(s):  
Radar System ◽  
Moving Targets ◽  
2D Ir ◽  
Detection And Tracking ◽  
Uwb Radar ◽  
Location Detection

scholarly journals Sensor Selection and Integration to Improve Video Segmentation in Complex Environments

2014 ◽  
Vol 2014 ◽  
pp. 1-14 ◽  
Author(s):  
Adam R. Reckley ◽  
Wei-Wen Hsu ◽  
Chung-Hao Chen ◽  
Gangfeng Ma ◽  
E-Wen Huang
Keyword(s):  
Object Detection ◽  
Background Subtraction ◽  
Video Segmentation ◽  
State Of The Art ◽  
Data Sets ◽  
Complex Data ◽  
Dynamic Background ◽  
Object Detection And Tracking ◽  
Detection And Tracking ◽  
Complex Data Sets

Background subtraction is often considered to be a required stage of any video surveillance system being used to detect objects in a single frame and/or track objects across multiple frames in a video sequence. Most current state-of-the-art techniques for object detection and tracking utilize some form of background subtraction that involves developing a model of the background at a pixel, region, or frame level and designating any elements that deviate from the background model as foreground. However, most existing approaches are capable of segmenting a number of distinct components but unable to distinguish between the desired object of interest and complex, dynamic background such as moving water and high reflections. In this paper, we propose a technique to integrate spatiotemporal signatures of an object of interest from different sensing modalities into a video segmentation method in order to improve object detection and tracking in dynamic, complex scenes. Our proposed algorithm utilizes the dynamic interaction information between the object of interest and background to differentiate between mistakenly segmented components and the desired component. Experimental results on two complex data sets demonstrate that our proposed technique significantly improves the accuracy and utility of state-of-the-art video segmentation technique.

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