Color-based Object Tracking Research Articles

Color-Based Object Tracking Using Forward and Inverse Kinematics

Nowadays, the current surge in diverse applications of robotic arms makes comprehending these concepts of paramount importance. Various studies have been conducted on the topic of robotic arms. However, little attention was paid to the coupling relationship between object tracking and kinematics. Therefore, this research paper delves into the intricacies of robotic arm dynamics, with a specific focus on the topics of Forward and Inverse Kinematics, alongside object tracking facilitated by color-based methodologies to deepen foundational insights into robotic arm functionality. Controlling the motion and movement of robotic arms is the initial step. Leveraging the capabilities of cutting-edge technologies such as OpenCV, Armpi, and Hiwonder, it becomes feasible to precisely rotate the servos to user-input degrees and accurately position the arm based on three-dimensional Cartesian coordinates. Additionally, cameras equipped in the system enable photographing and recognizing target objects. By analyzing various positions of the target object, the robotic arm can dynamically adjust multiple servos to maintain the object's relative position. This tracking mechanism relies on the Proportional-Integral-Derivative algorithm, incorporating a minimum valid area parameter to mitigate interferences. Harnessing these algorithms empowers the robotic arm to discern objects based on camera-detected colors, and is affected slightly by different angles or distances, meaning the system's error margin consistently resides within a favorable interval. The approach exhibits practical utility in daily life scenarios, such as drug classification and recognition.

Accuracy and feasibility of a novel fine hand motor skill assessment using computer vision object tracking

We developed a computer vision-based three-dimension (3D) motion capture system employing two action cameras to examine fine hand motor skill by tracking an object manipulated by a hand. This study aimed to examine the accuracy and feasibility of this approach for detecting changes in a fine hand motor skill. We conducted three distinct experiments to assess the system's accuracy and feasibility. We employed two high-resolution, high-frame-rate action cameras. We evaluated the accuracy of our system in calculating the 3D locations of moving object in various directions. We also examined the system's feasibility in identifying improvement in fine hand motor skill after practice in eleven non-disabled young adults. We utilized color-based object detection and tracking to estimate the object's 3D location, and then we computed the object's kinematics, representing the endpoint goal-directed arm reaching movement. Compared to ground truth measurements, the findings demonstrated that our system can adequately estimate the 3D locations of a moving object. We also showed that the system can be used to measure the endpoint kinematics of goal-directed arm reaching movements to detect changes in fine hand motor skill after practice. Future research is needed to confirm the system's reliability and validity in assessing fine hand motor skills in patient populations.

A Position Controller Model on Color-Based Object Tracking using Fuzzy Logic

Robotics vision is applying technology on the camera to view the environmental conditions as well as the function of the human eye. Colour object tracking system is one application of robotics vision technology with the ability to follow the object being detected. Several methods have been used to generate a good response position control, but most are still using conventional control approach. Fuzzy logic which includes several step of which is to determine the value of crisp input must be fuzzification. The output of fuzzification is forwarded to the process of inference in which there are some fuzzy logic rules. The inference output forwarded to the process of defuzzification to be transformed into outputs (crisp output) to drive the servo motors on the X-axis and Y-axis. Fuzzy logic control is applied to the color-based object tracking system, the system is successful to follow a moving object with average speed of 7.35 cm/s in environments with 117 lux light intensity.

Color based Object Tracking Robot

Color based Object Tracking Robot

Color-Based Object Tracking For Unmanned Aerial Vehicles

Color-Based Object Tracking For Unmanned Aerial Vehicles

Investigation of Threading Building Blocks Framework on Real Time Visual Object Tracking Algorithm

Object tracking plays important roles in various applications such as surveillance, search and rescue, augmented reality and robotics. This paper presents an investigation on multi-threading framework capability for color-based object tracking applications. A multi-threading framework based on Threading Building Blocks (TBB) was implemented on a multi-core system to enhance the image processing performance on a real-time visual object tracking algorithm. Intel Parallel Studio was used to implement this parallel framework. The performance between sequential and multi-threading framework was evaluated and compared. We demonstrated the multi-threading framework was approximately two times faster when compared to the sequential framework in our experiments.

Fast and effective color-based object tracking by boosted color distribution

In this paper, we propose a novel tracking algorithm, boosted color distribution (BCD), for tracking color objects. There exist three contributions in this paper. First, we propose a novel online gentle boost (OGB) algorithm for online learning. The essential idea of OGB is composed of two aspects: online updating candidate weak classifiers, and then choosing and combining them in a boosting way. Second, we design a novel weak classifier, log color feature distribution ratio, which focuses on the difference of color distributions rather than individual samples and provides a simple yet effective manner of mining color features for object tracking. Third, by combining our OGB algorithm and our log color features, we develop a fast yet effective color-based object tracking algorithm. Numerous experiments demonstrate that our tracking algorithm is better than or not worse than some state-of-the-art tracking algorithms on some public sequences.Overall, this paper presents a novel BCD algorithm for color object tracking that achieves good results at a fast speed.

A new algorithm to rigid and non-rigid object tracking in complex environments

With a focus on complex environments, the present paper describes a new algorithm in rigid and non-rigid object tracking through color feature. Object tracking in these environments is taken into consideration as real-time applications, such as manufacturing, surveillance and monitoring, smart rooms, and so on, where partial or full occlusion sensibly occurs. As is obvious, the best color-based object tracking algorithm is now known, as the mean shift (MS) iterative procedure, to find the location of an object in image sequences. The algorithm performance is not unfortunately acceptable once objects in complex environments need to be tracked. In fact, the main aim of the present research is to improve the MS tracking algorithm, by proposing an improved convex kernel function, which is now realized in association with the Kalman filter approach (KFA). In the algorithm proposed here, the KFA is employed to solve the full occlusion problems since the speed for the objects is constant. Subsequently, the present investigated robust kernel function has been designed to dominate the low saturation and partial occlusion problems.

Autonomous Hovering and Landing of a Quad-rotor Micro Aerial Vehicle by Means of on Ground Stereo Vision System

On ground stereo vision system is used for autonomous hovering and landing of a quadrotor Micro Aerial Vehicle (MAV). This kind of system has an advantage to support embedded vision system for autonomous hovering and landing, since an embedded vision system occasionally gives inaccurate distance calculation due to either vibration problem or unknown geometry of the landing target. Color based object tracking by using Continuously Adaptive Mean Shift (CAMSHIFT) algorithm was examined. Nonlinear model of quad-rotor MAV and a PID controller were used for autonomous hovering and landing. The result shows that the Camshift based object tracking algorithm has good performance. Additionally, the comparison between the stereo vision system based and GPS based autonomous hovering of a quad-rotor MAV shows that stereo vision system has better performance. The accuracy of the stereo vision system is about 1 meter in the longitudinal and lateral direction when the quad-rotor flies in 6 meters of altitude. In the same experimental condition, the GPS based system accuracy is about 3 meters. Additionally, experiment on autonomous landing gives a reliable result.

2B35 Vision-based Object Tracking by Micro-Air Vehicle

This paper describes an object tracking system using an autonomous Micro Air Vehicle (MAV) and demonstrate its potential use for civilian purposes. The vision-based control system relies on color and feature based vision algorithm for target detection and tracking, Kalman filters for relative pose estimation, and a nonlinear controller for MAV stabilization and guidance. The color based vision algorithm uses simple and fast technique object color detection using integral images, while optical flow is utilized for feature tracking. Each vision algorithm has its own weakness depending on the environment when vision-based tracking is done. The color based object tracking very much depends on suitable light condition, and the tracked object and its background color. The feature based object tracking works well with object with strong features but risks loss of detection when there is noise in the streaming image while tracking. An algorithm that combines the two approaches is presented to compensate each single vision algorithm weakness. The vision algorithm relies on information from a single on-board camera. An arbitrary target can be selected in real-time from the ground control station, thereby outperforming template and learning-based approaches. Experimental results obtained from outdoor (light tests, showed that the vision-control system enabled the MAV to track and hover above the target as long as the battery is available. We also propose this object tracking system to track a moving object on the ground as well in leader-following system setup.

Bilateral learning for color-based tracking

This paper addresses the issue of color model adaptation and color-based object tracking in a dynamic scene. Under different environmental conditions such as illumination changes, a static color model is inadequate for the purpose of color-based object detection and tracking. Color model adaptation is required and this has to be integrated into the tracking procedure within the spatial domain. To track a target in both the color and spatial domains, a Bilateral Learning (BL) approach is proposed in this paper. Formulated as an unsupervised learning problem, the adaptations of the color and spatial models are fitted into an EM framework by updating in the color and image spaces alternately. This results in the adaptation of the color model and the localization of the target along the image sequence. Experimental results show the effectiveness and efficacy of the proposed approach for color model adaptation and object tracking under illumination changes and environmental noise in real time.

Color in Image and Video Processing: Most Recent Trends and Future Research Directions

The motivation of this paper is to provide an overview of the most recent trends and of the future research directions in color image and video processing. Rather than covering all aspects of the domain this survey covers issues related to the most active research areas in the last two years. It presents the most recent trends as well as the state-of-the-art, with a broad survey of the relevant literature, in the main active research areas in color imaging. It also focuses on the most promising research areas in color imaging science. This survey gives an overview about the issues, controversies, and problems of color image science. It focuses on human color vision, perception, and interpretation. It focuses also on acquisition systems, consumer imaging applications, and medical imaging applications. Next it gives a brief overview about the solutions, recommendations, most recent trends, and future trends of color image science. It focuses on color space, appearance models, color difference metrics, and color saliency. It focuses also on color features, color-based object tracking, scene illuminant estimation and color constancy, quality assessment and fidelity assessment, color characterization and calibration of a display device. It focuses on quantization, filtering and enhancement, segmentation, coding and compression, watermarking, and lastly on multispectral color image processing. Lastly, it addresses the research areas which still need addressing and which are the next and future perspectives of color in image and video processing.