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

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

3D Camouflaging Object using RGB-D Sensors

This paper proposes a new optical camouflage system that uses RGB-D cameras, for acquiring point cloud of background scene, and tracking observers eyes. This system enables a user to conceal an object located behind a display that surrounded by 3D objects. If we considered here the tracked point of observer s eyes is a light source, the system will work on estimating shadow shape of the display device that falls on the objects in background. The system uses the 3d observer s eyes and the locations of display corners to predict their shadow points which have nearest neighbors in the constructed point cloud of background scene.Comment: 6 pages, 12 figures, 2017 IEEE International Conference on SM

A graphical model based solution to the facial feature point tracking problem

In this paper a facial feature point tracker that is motivated by applications such as human-computer interfaces and facial expression analysis systems is proposed. The proposed tracker is based on a graphical model framework. The facial features are tracked through video streams by incorporating statistical relations in time as well as spatial relations between feature points. By exploiting the spatial relationships between feature points, the proposed method provides robustness in real-world conditions such as arbitrary head movements and occlusions. A Gabor feature-based occlusion detector is developed and used to handle occlusions. The performance of the proposed tracker has been evaluated on real video data under various conditions including occluded facial gestures and head movements. It is also compared to two popular methods, one based on Kalman filtering exploiting temporal relations, and the other based on active appearance models (AAM). Improvements provided by the proposed approach are demonstrated through both visual displays and quantitative analysis

Age estimates of isochronous reflection horizons by combining ice core, survey, and synthetic radar data.

Ice core records and ice-penetrating radar data contain complementary information on glacial subsurface structure and composition, providing various opportunities for interpreting past and present environmental conditions. To exploit the full range of possible applications, accurate dating of internal radar reflection horizons and knowledge about their constituting features is required. On the basis of three ice core records from Dronning Maud Land, Antarctica, and surface-based radar profiles connecting the drilling locations, we investigate the accuracies involved in transferring age-depth relationships obtained from the ice cores to continuous radar reflections. Two methods are used to date five internal reflection horizons: (1) conventional dating is carried out by converting the travel time of the tracked reflection to a single depth, which is then associated with an age at each core location, and (2) forward modeling of electromagnetic wave propagation is based on dielectric profiling of ice cores and performed to identify the depth ranges from which tracked reflections originate, yielding an age range at each drill site. Statistical analysis of all age estimates results in age uncertainties of 5 10 years for conventional dating and an error range of 1 16 years for forward modeling. For our radar operations at 200 and 250 MHz in the upper 100 m of the ice sheet, comprising some 1000 1500 years of deposition history, final age uncertainties are 8 years in favorable cases and 21 years at the limit of feasibility. About one third of the uncertainty is associated with the initial ice core dating; the remaining part is associated with radar data quality and analysis