Hierarchical fuzzy logic based approach for object tracking

In this paper a novel tracking approach based on fuzzy concepts is introduced. A methodology for both single and multiple object tracking is presented. The aim of this methodology is to use these concepts as a tool to, while maintaining the needed accuracy, reduce the complexity usually involved in object tracking problems. Several dynamic fuzzy sets are constructed according to both kinematic and non-kinematic properties that distinguish the object to be tracked. Meanwhile kinematic related fuzzy sets model the object's motion pattern, the non-kinematic fuzzy sets model the object's appearance. The tracking task is performed through the fusion of these fuzzy models by means of an inference engine. This way, object detection and matching steps are performed exclusively using inference rules on fuzzy sets. In the multiple object methodology, each object is associated with a confidence degree and a hierarchical implementation is performed based on that confidence degree.info:eu-repo/semantics/publishedVersio

CoMaL Tracking: Tracking Points at the Object Boundaries

Traditional point tracking algorithms such as the KLT use local 2D information aggregation for feature detection and tracking, due to which their performance degrades at the object boundaries that separate multiple objects. Recently, CoMaL Features have been proposed that handle such a case. However, they proposed a simple tracking framework where the points are re-detected in each frame and matched. This is inefficient and may also lose many points that are not re-detected in the next frame. We propose a novel tracking algorithm to accurately and efficiently track CoMaL points. For this, the level line segment associated with the CoMaL points is matched to MSER segments in the next frame using shape-based matching and the matches are further filtered using texture-based matching. Experiments show improvements over a simple re-detect-and-match framework as well as KLT in terms of speed/accuracy on different real-world applications, especially at the object boundaries.Comment: 10 pages, 10 figures, to appear in 1st Joint BMTT-PETS Workshop on Tracking and Surveillance, CVPR 201

Object detection and tracking in video image

In recent days, capturing images with high quality and good size is so easy because of rapid improvement in quality of capturing device with less costly but superior technology. Videos are a collection of sequential images with a constant time interval. So video can provide more information about our object when scenarios are changing with respect to time. Therefore, manually handling videos are quite impossible. So we need an automated devise to process these videos. In this thesis one such attempt has been made to track objects in videos. Many algorithms and technology have been developed to automate monitoring the object in a video file. Object detection and tracking is a one of the challenging task in computer vision. Mainly there are three basic steps in video analysis: Detection of objects of interest from moving objects, Tracking of that interested objects in consecutive frames, and Analysis of object tracks to understand their behavior. Simple object detection compares a static background frame at the pixel level with the current frame of video. The existing method in this domain first tries to detect the interest object in video frames. One of the main difficulties in object tracking among many others is to choose suitable features and models for recognizing and tracking the interested object from a video. Some common choice to choose suitable feature to categories, visual objects are intensity, shape, color and feature points. In this thesis, we studied about mean shift tracking based on the color pdf, optical flow tracking based on the intensity and motion; SIFT tracking based on scale invariant local feature points. Preliminary results from experiments have shown that the adopted method is able to track targets with translation, rotation, partial occlusion and deformation

A Monocular SLAM Method to Estimate Relative Pose During Satellite Proximity Operations

Automated satellite proximity operations is an increasingly relevant area of mission operations for the US Air Force with potential to significantly enhance space situational awareness (SSA). Simultaneous localization and mapping (SLAM) is a computer vision method of constructing and updating a 3D map while keeping track of the location and orientation of the imaging agent inside the map. The main objective of this research effort is to design a monocular SLAM method customized for the space environment. The method developed in this research will be implemented in an indoor proximity operations simulation laboratory. A run-time analysis is performed, showing near real-time operation. The method is verified by comparing SLAM results to truth vertical rotation data from a CubeSat air bearing testbed. This work enables control and testing of simulated proximity operations hardware in a laboratory environment. Additionally, this research lays the foundation for autonomous satellite proximity operations with unknown targets and minimal additional size, weight, and power requirements, creating opportunities for numerous mission concepts not previously available

Motion Trajectories of Over-Height Vehicles for Warning Drivers

Collision of over-height vehicles with low bridges and tunnels occur with high frequency in the UK as many structures were built at a time when there was less moving traffic on the roadway. These older bridges are now considered at risk of vehicular strikes due to its low clearance height (less than 16 feet 6 inches or 5.03 metres). While previous methods have used vision-based systems to address the over-height warning problem, such methods are sensitive to wind. In this paper, we proposed an extension of the work done to minimise false detections due to wind by using a constraint-based method to track motion trajectories to improve the overall performance of the system. The dataset consists of 102 over-height vehicles recorded at 25 fps. The paper compares feature detectors to optimally track vehicle trajectories and analyses its motion to accurately classify positive detections. The final validation yields a performance of 94.5% recall and 91.1% precision.Career Integration Grants (CIG) - Marie Curie Action

A hierarchical strategy for real-time tracking on-board UAVs

In this paper, we present a real-time tracking strategy based on direct methods for tracking tasks on-board UAVs, that is able to overcome problems posed by the challenging conditions of the task: e.g. constant vibrations, fast 3D changes, and limited capacity on-board. The vast majority of approaches make use of feature-based methods to track objects. Nonetheless, in this paper we show that although some of these feature-based solutions are faster, direct methods can be more robust under fast 3D motions (fast changes in position), some changes in appearance, constant vibrations (without requiring any specific hardware or software for video stabilization), and situations where part of the object to track is out the field of view of the camera. The performance of the proposed strategy is evaluated with images from real-flight tests using different evaluation mechanisms (e.g. accurate position estimation using a Vicon sytem). Results show that our tracking strategy performs better than well known feature-based algorithms and well known configurations of direct methods, and that the recovered data is robust enough for vision-in-the-loop tasks