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

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

Open and closed contours tracking based on shape priors and training

학위논문 (박사)-- 서울대학교 대학원 : 전기·컴퓨터공학부, 2016. 8. 조남익.This dissertation presents a new open and closed contours tracking algorithm using shape prior and its training based on a Bayesian framework, where the contour is a part (open contour) or the whole (closed contour) of the object's boundary. The shape of an object is a very important feature for many vision tasks such as object recognition and tracking. Specifically, the tracking performance can be increased if the target is determined and the tracker utilizes its shape information. The proposed method provides a new state space model for the representation of contours, which can reflect the shape information to the contour and handle rigid and non-rigid motions of contours independently. This model enables us to focus on the non-rigid motion during the tracking, and the model works for challenging rigid motion scenarios. In addition, for the robust tracking of contours, a measurement function that considers the contrast on object boundaries, target appearance, and temporal coherence is proposed. The proposed method is tested for various cases of contours such as open contour, closed contour and multi-contours. The state space model and measurement functions are modified a little bit in consideration of each contour model. First, an open contour is modeled and tracked by the proposed method, which has received little attention during several decades compared with the closed contour or bounding box shape tracking. The proposed state space model can represent an open contour that is moved by the dynamic model where rigid and non-rigid motions are absolutely separated. The measurement is designed with contrast, local track and appearance terms that indicate the proper position of the target and make the tracking more robust. The proposed method is applied to two examples of open contours targets (human omega shape and a cheetah profile), and experimental results show that the proposed method achieves superior performance to the conventional contour tracking methods. The proposed method is also compared with recent bounding box tracking methods for the object tracking purposes, and the comparison shows that the proposed method works robustly to fast motions and yields more accurate estimate of object's location than the conventional bounding box tracking methods. Second, the proposed method is tested for the closed contour tracking which is usually carried out by segmentation algorithms or level set methods. A closed contour is described by the proposed model and deformed by the dynamic model. Measurement function is the same to the case of open contour tracking except the local track term, which is calculated with partial object appearances that are denoted by some local patches and their relative positions. As an application example, automobiles in blackbox video sequences are tracked by the proposed method. Experimental results show that the proposed method accomplishes higher performance than conventional tracking methods where some of them presents the target as a bounding box and others extract the object boundary using segmentation methods. Moreover, the document capture and tracking algorithm is also proposed, which is suitable for applying the proposed method because the shape of document is well known (a quadrilateral) and its boundary can be estimated by the proposed method. This system is based on building quadrilaterals as document proposals using line segment detector and tests all proposals to find the best one with measurement terms. The proposed algorithm makes good marks at 2015 ICDAR competition. Finally, multi-contours tracking algorithm is devised based on the contour tracking method. It is assumed that targets belong to the same category and their appearances, colors and shapes are similar to each other. Thus, the proposed method trains only one shape model to track multi-contours. The state space vector is amended such that all contours can be represented by one state vector. In order to consider interactions between targets, the interaction term is attached to the existing dynamic model. As an example, human legs are tracked by the proposed method which may help to recognize the gaits. Experimental results show that conventional algorithms have troubles in tracking and distinguishing between the two legs, whereas all targets are well estimated accurately by the proposed method.Chapter 1 Introduction 1 1.1 Open contour tracking based on a nonrigid shape training 6 1.2 Target-based closed contour tracking 7 1.3 Multi-contours tracking for objects that belong in the same category 8 1.4 Structure of the dissertation 10 Chapter 2 Related work 11 2.1 Bounding box tracking 11 2.2 Contour tracking 12 2.3 Multi-objects tracking 14 Chapter 3 Open contour tracking based on a nonrigid shape training 15 3.1 Proposed state space model 15 3.1.1 Reviews on the active contour model 15 3.1.2 Proposed state vector 16 3.1.3 Proposed stochastic dynamic model 18 3.2 Training of the proposed state space model 20 3.2.1 Training criterion 22 3.2.2 Optimization method 23 3.2.3 Proof for solving the training problem 24 3.3 Measurement 27 3.3.1 Contrast term 27 3.3.2 Local track term 29 3.3.3 Appearance term 29 3.3.4 Model update 31 3.3.5 Weights of three measurement terms 31 3.4 Experimental results 34 3.4.1 Parameter selection 34 3.4.2 Label map construction 36 3.4.3 Comparison with existing contour-based methods 37 3.4.4 Comparison with bounding box based methods 42 3.4.5 Comparison with tracking methods for nonrigid objects 48 Chapter 4 Target-dependent closed contour tracking 51 4.1 The proposed model 51 4.1.1 Active contour model 51 4.1.2 Contour dynamics 52 4.2 Measurement 54 4.2.1 Local track term 54 4.3 Experimental results 57 4.3.1 Label map construction 59 4.3.2 Comparison to conventional tracking methods 59 4.4 Special case : document capture 65 4.4.1 Document model 65 4.4.2 Document proposals 66 4.4.3 Measurement 67 4.4.4 Renement . 69 4.4.5 Experimental results 70 Chapter 5 Multi-contours tracking for objects that belong to the same category 83 5.1 Proposed multi-contours tracking 83 5.1.1 State space model 84 5.1.2 Dynamics and measurement . 86 5.1.3 Particle sampling . 88 5.2 Experimental results 89 5.2.1 Comparison with other multi-objects tracking methods . 92 5.2.2 Comparison with tracking methods for a single object . 98 Chapter 6 Conclusions 101 Bibliography 103 Abstract (Korean) 109Docto

Layered graphical models for tracking partially-occluded moving objects in video (PhD thesis)

Tracking multiple targets using fixed cameras with non-overlapping views is a challenging problem. One of the challenges is predicting and tracking through occlusions caused by other targets or by fixed objects in the scene. Considerable effort has been devoted toward developing appearance models that are robust to partial occlusions, tracking algorithms that cope with short-term loss of observations, and algorithms that learn static occlusion maps. In this thesis we consider scenarios where it is impossible to learn a static occlusion map. This is often the case when the scene consists of both people and large objects whose position is not permanently fixed. These objects may enter, leave or relocate within the scene during a short time span. We call such objects "relocatable objects" or "relocatable occluders." We develop a representation for scenes containing relocatable objects that can cause partial occlusions of people in a camera's field of view. In many practical applications, relocatable objects tend to appear often; therefore, models for them can be learned off-line and stored in a database. We formulate an occluder-centric representation, called a graphical model layer, where a person's motion in the ground plane is defined as a first-order Markov process on activity zones, while image evidence is aggregated in 2D observation regions that are depth-ordered with respect to the occlusion mask of the relocatable object. We represent real-world scenes as a composition of depth-ordered, interacting graphical model layers, and account for image evidence in a way that handles mutual overlap of the observation regions and their occlusions by the relocatable objects. These layers interact: proximate ground plane zones of different model instances are linked to allow a person to move between the layers, and image evidence is shared between the observation regions of these models. We demonstrate our formulation in tracking low-resolution, partially-occluded pedestrians in the vicinity of parked vehicles. In these scenarios some tracking formulations that rely on part-based person detectors may fail completely. Our pedestrian tracker fares well and compares favorably with the state-of-the-art pedestrian detectors---lowering false positives by twenty-nine percent and false negatives by forty-two percent---and a deformable-contour--based tracker