Comparison of different integral histogram based tracking algorithms

Object tracking is an important subject in computer vision with a wide range of applications – security and surveillance, motion-based recognition, driver assistance systems, and human-computer interaction. The proliferation of high-powered computers, the availability of high quality and inexpensive video cameras, and the increasing need for automated video analysis have generated a great deal of interest in object tracking algorithms. Tracking is usually performed in the context of high-level applications that require the location and/or shape of the object in every frame. Research is being conducted in the development of object tracking algorithms over decades and a number of approaches have been proposed. These approaches differ from each other in object representation, feature selection, and modeling the shape and appearance of the object. Histogram-based tracking has been proved to be an efficient approach in many applications. Integral histogram is a novel method which allows the extraction of histograms of multiple rectangular regions in an image in a very efficient manner. A number of algorithms have used this function in their approaches in the recent years, which made an attempt to use the integral histogram in a more efficient manner. In this paper different algorithms which used this method as a part of their tracking function, are evaluated by comparing their tracking results and an effort is made to modify some of the algorithms for better performance. The sequences used for the tracking experiments are of gray scale (non-colored) and have significant shape and appearance variations for evaluating the performance of the algorithms. Extensive experimental results on these challenging sequences are presented, which demonstrate the tracking abilities of these algorithms

Visual object tracking performance measures revisited

The problem of visual tracking evaluation is sporting a large variety of performance measures, and largely suffers from lack of consensus about which measures should be used in experiments. This makes the cross-paper tracker comparison difficult. Furthermore, as some measures may be less effective than others, the tracking results may be skewed or biased towards particular tracking aspects. In this paper we revisit the popular performance measures and tracker performance visualizations and analyze them theoretically and experimentally. We show that several measures are equivalent from the point of information they provide for tracker comparison and, crucially, that some are more brittle than the others. Based on our analysis we narrow down the set of potential measures to only two complementary ones, describing accuracy and robustness, thus pushing towards homogenization of the tracker evaluation methodology. These two measures can be intuitively interpreted and visualized and have been employed by the recent Visual Object Tracking (VOT) challenges as the foundation for the evaluation methodology

PixelTrack: A Fast Adaptive Algorithm for Tracking Non-rigid Objects

International audienceIn this paper, we present a novel algorithm for fast tracking of generic objects in videos. The algorithm uses two components: a detector that makes use of the generalised Hough transform with pixel-based descriptors, and a probabilistic segmentation method based on global models for foreground and background. These components are used for tracking in a combined way, and they adapt each other in a co-training manner. Through effective model adaptation and segmentation, the algorithm is able to track objects that undergo rigid and non-rigid deformations and considerable shape and appearance variations. The proposed tracking method has been thoroughly evaluated on challenging standard videos, and outperforms state-of-theart tracking methods designed for the same task. Finally, the proposed models allow for an extremely efficient implementation, and thus tracking is very fast

TRIC-track: tracking by regression with incrementally learned cascades

This paper proposes a novel approach to part-based track- ing by replacing local matching of an appearance model by direct prediction of the displacement between local image patches and part locations. We propose to use cascaded regression with incremental learning to track generic objects without any prior knowledge of an object’s structure or appearance. We exploit the spatial constraints between parts by implicitly learning the shape and deformation parameters of the object in an online fashion. We integrate a multiple temporal scale motion model to initialise our cascaded regression search close to the target and to allow it to cope with occlusions. Experimental results show that our tracker ranks first on the CVPR 2013 Benchmark

Advances in MEMS IMU Cluster Technology for Small Satellite Applications

In recent years, there has been increased interest in Micro-Electro Mechanical Systems (MEMS) Inertial Measurement Units (IMUs) due to their relatively small volumetric footprint and low-cost. Although this advantage far outweighs the volumetric footprint and cost of traditional high-performance IMUs, MEMS technology has yet to match the performance of such devices. In spite of this, it has been shown in theory that a cluster of MEMS IMUs may signicantly improve the performance over a single MEMS IMU. To further develop this theory, two prototype boards have been designed and constructed that include 16 MEMS accelerometers and gyroscopes af- xed to a single Printed Circuit Board (PCB). To prove this technology, hardware and software has been developed for calibration and fault detection, which represents the majority of the body of this thesis. An apparatus has been designed to easily acquire three-axis measurements from the cluster prototype on a single-axis rate table. These measurements may then be placed into a Maximum Likelihood Estimation (MLE) algorithm in order to acquire the necessary error coecients incorporated in IMU measurements. Once these error coecients are accurately determined, future measurements may be calibrated. Finally, a fault detection, isolation, and recovery (FDIR) architecture was developed and simulated to determine faulty measurements in real-time, so that bad measurements may not be placed into downstream navigation lters. The hardware, software, and testing developed and performed in this thesis will be used in the verication process of an IMU cluster to help prove its worthiness in modern day small satellite applications