Target Appearance Model Research Articles

Real-Time Tracking by Double Templates Matching Based on Timed Motion History Image with HSV Feature

It is a challenge to represent the target appearance model for moving object tracking under complex environment. This study presents a novel method with appearance model described by double templates based on timed motion history image with HSV color histogram feature (tMHI-HSV). The main components include offline template and online template initialization, tMHI-HSV-based candidate patches feature histograms calculation, double templates matching (DTM) for object location, and templates updating. Firstly, we initialize the target object region and calculate its HSV color histogram feature as offline template and online template. Secondly, the tMHI-HSV is used to segment the motion region and calculate these candidate object patches' color histograms to represent their appearance models. Finally, we utilize the DTM method to trace the target and update the offline template and online template real-timely. The experimental results show that the proposed method can efficiently handle the scale variation and pose change of the rigid and nonrigid objects, even in illumination change and occlusion visual environment.

Online multiple instance gradient feature selection for robust visual tracking

In this paper, we focus on learning an adaptive appearance model robustly and effectively for object tracking. There are two important factors to affect object tracking, the one is how to represent the object using a discriminative appearance model, the other is how to update appearance model in an appropriate manner. In this paper, following the state-of-the-art tracking techniques which treat object tracking as a binary classification problem, we firstly employ a new gradient-based Histogram of Oriented Gradient (HOG) feature selection mechanism under Multiple Instance Learning (MIL) framework for constructing target appearance model, and then propose a novel optimization scheme to update such appearance model robustly. This is an unified framework that not only provides an efficient way of selecting the discriminative feature set which forms a powerful appearance model, but also updates appearance model in online MIL Boost manner which could achieve robust tracking overcoming the drifting problem. Experiments on several challenging video sequences demonstrate the effectiveness and robustness of our proposal.

Studentized Dynamical System for Robust Object Tracking

This paper describes a studentized dynamical system (SDS) for robust target tracking using a subspace representation. Dynamical systems (DS) provide a powerful framework for the probabilistic modeling of temporal sequences. Visual tracking problems are often cast as a sequential inference problem within the DS framework and a compact way to model the observation distributions (i.e., object appearances) is through probabilistic principal component analysis (PPCA). PPCA is a classic Gaussian based subspace representation method and a popular tool for appearance modeling. Although Gaussian density has theoretically nice properties, resulting in models that are always tractable, they are also severely limited in practical settings. One of the central issues in the use of PPCA for target appearance modeling is that it is very sensitive to outliers. The Gaussian density has a very light tail, while real world data with outliers exhibit heavy tails. Recently, more heavy-tailed distributions (e.g., Student's t-distribution) have been introduced to increase the robustness of the original PPCA. We propose to augment the traditional target tracking DS by adding a set of auxiliary latent variables to adjust the shape of the observation distribution. We show that by carefully choosing the probability density of these auxiliary latent variables, a more robust observation distribution can be obtained with tails heavier than Gaussian. Numerical experiments verify that the proposed SDS has a better capability to handle considerable amount of outlier noise and an improved tracking performance over DS with a Gaussian based observation model.

Pedestrian detection and tracking based on SACON background model

A new background modeling method based on Sample Consensus (SACON) was proposed for detecting foreground objects. The method, which has higher accuracy than other five popular methods in the experiment when seven typical video sequences were adopted, is suit for detecting and tracking the moving target under complex circumstances. On pedestrian detection and tracking, considering the target to be non-rigid, a new target appearance model containing spatial domain and color information was built based on SACON. The experimental results show that the method can describe pedestrian feature exactly and can detect or track targets with barriers and similar color and small targets with high accuracy.

Probabilistic fusion-based parameter estimation for visual tracking

In object tracking, visual features may not be discriminative enough to estimate high dimensional motion parameters accurately, and complex motion estimation is computationally expensive due to a large search space. To tackle these problems, a reasonable strategy is to track small components within the target independently in lower dimensional motion parameter spaces (e.g., translation only) and then estimate the overall high dimensional motion (e.g., translation, scale and rotation) by statistically integrating the individual tracking results. Although tracking each component in a lower dimensional space is more reliable and faster, it is not trivial to combine the local motion information and estimate global parameters in a robust way because the individual component motions are frequently inconsistent. We propose a robust fusion algorithm to estimate the complex motion parameters using variable-bandwidth mean-shift. By employing correlation-based uncertainty modeling and fusion of individual components, the motion parameter that is robust to outliers can be detected with variable-bandwidth density-based fusion (VBDF) algorithm. In addition, we describe a method to update target appearance model for each component adaptively based on the component motion consistency. We present various tracking results and compare the performance of our algorithm with others using real video sequences.

Sequential Kernel Density Approximation and Its Application to Real-Time Visual Tracking

Visual features are commonly modeled with probability density functions in computer vision problems, but current methods such as a mixture of Gaussians and kernel density estimation suffer from either the lack of flexibility, by fixing or limiting the number of Gaussian components in the mixture, or large memory requirement, by maintaining a non-parametric representation of the density. These problems are aggravated in real-time computer vision applications since density functions are required to be updated as new data becomes available. We present a novel kernel density approximation technique based on the mean-shift mode finding algorithm, and describe an efficient method to sequentially propagate the density modes over time. While the proposed density representation is memory efficient, which is typical for mixture densities, it inherits the flexibility of non-parametric methods by allowing the number of components to be variable. The accuracy and compactness of the sequential kernel density approximation technique is illustrated by both simulations and experiments. Sequential kernel density approximation is applied to on-line target appearance modeling for visual tracking, and its performance is demonstrated on a variety of videos.

Autonomous mobile target tracking system based on grey-fuzzy control algorithm

This paper presents a new position-based tracking system for autonomous mobile target tracking task. A grey-fuzzy controller (GFC) is developed for motion control of the tracker, in which dynamics models of the target and tracker are not required a priori. The target detection is based on the adaptive visual detector (AVD), which can online adjust the histogram model based on the change of surrounding conditions, such as light variation, in a natural environment. The AVD and GFC are integrated together for mobile target-tracking applications. There are several advantages of the integrated system, in particular: (1) it can rapidly learn the target appearance model for the detection involved with the tracking task; (2) the temporal dynamics model of the target motion can be approximated for the predictive localization of the moving target; and (3) the system can deal with the uncertain environmental conditions to ensure the tracking performance by GFC. Three mobile robots in the authors' laboratory have been used to demonstrate the success of this integrated system experimentally. They also conduct target tracking experiments, in which Chung Cheng-I tracks various moving targets. The results demonstrate the robustness and flexibility of the overall system in dealing with mobile target-tracking problems under varied natural environment conditions.