Appearance Model For Tracking Research Articles

Object tracking with hierarchical multiview learning

Building a robust appearance model is useful to improve tracking performance. We propose a hierarchical multiview learning framework to construct the appearance model, which has two layers for tracking. On the top layer, two different views of features, grayscale value and histogram of oriented gradients, are adopted for representation under the cotraining framework. On the bottom layer, for each view of each feature, three different random subspaces are generated to represent the appearance from multiple views. For each random view submodel, the least squares support vector machine is employed to improve the discriminability for concrete and efficient realization. These two layers are combined to construct the final appearance model for tracking. The proposed hierarchical model assembles two types of multiview learning strategies, in which the appearance can be described more accurately and robustly. Experimental results in the benchmark dataset demonstrate that the proposed method can achieve better performance than many existing state-of-the-art algorithms.

Rapidly constructed appearance models for tracking in augmented reality applications

This paper presents a method for rapidly generating crude, appearance-based edge models consisting of a set of planes. The appearance of each plane is modeled using a set of keyframes containing a list of edgels. These models are generated from a short video sequence with a few annotated frames indicating the location of the object of interest. The data requires 3–5 min to collect using a handheld device instrumented with a camera. The models produced are used with an existing edge tracking algorithm modified to select the appropriate edge keyframe and detect occlusion. A framestore is also created containing several views of the object represented as sets of point features. The framestore is used to provide an initial, rough pose estimate for initializing contour tracking. The presented system is used to create an augmented reality application to guide a user through a machine tool setup and a printer maintenance task. The models are shown to be an accurate representation of the object. Additionally, the performance of various aspects of the model making and tracking algorithms are evaluated.

Introduction to the special section on video surveillance

UTOMATED video surveillance addresses real-time observation of people and vehicles within a busy environment, leading to a description of their actions and interactions. The technical issues include moving object detection and tracking, object classification, human motion analysis, and activity understanding, touching on many of the core topics of computer vision, pattern analysis, and aritificial intelligence. Video surveillance has spawned large research projects in the United States, Europe, and Japan, and has been the topic of several international conferences and workshops in recent years. There are immediate needs for automated surveillance systems in commercial, law enforcement, and military applications. Mounting video cameras is cheap, but finding available human resources to observe the output is expensive. Although surveillance cameras are already prevalent in banks, stores, and parking lots, video data currently is used only “after the fact” as a forensic tool, thus losing its primary benefit as an active, real-time medium. What is needed is continuous 24-hour monitoring of surveillance video to alert security officers to a burglary in progress or to a suspicious individual loitering in the parking lot, while there is still time to prevent the crime. In addition to the obvious security applications, video surveillance technology has been proposed to measure traffic flow, detect accidents on highways, monitor pedestrian congestion in public spaces, compile consumer demographics in shopping malls and amusement parks, log routine maintainence tasks at nuclear facilities, and count endangered species. The numerous military applications include patrolling national borders, measuring the flow of refugees in troubled areas, monitoring peace treaties, and providing secure perimeters around bases and embassies. The 11 papers in this special section illustrate topics and techniques at the forefront of video surveillance research. These papers can be loosely organized into three categories. Detection and tracking involves real-time extraction of moving objects from video and continuous tracking over time to form persistent object trajectories. C. Stauffer and W.E.L. Grimson introduce unsupervised statistical learning techniques to cluster object trajectories produced by adaptive background subtraction into descriptions of normal scene activity. Viewpoint-specific trajectory descriptions from multiple cameras are combined into a common scene coordinate system using a calibration technique described by L. Lee, R. Romano, and G. Stein, who automatically determine the relative exterior orientation of overlapping camera views by observing a sparse set of moving objects on flat terrain. Two papers address the accumulation of noisy motion evidence over time. R. Pless, T. Brodský, and Y. Aloimonos detect and track small objects in aerial video sequences by first compensating for the self-motion of the aircraft, then accumulating residual normal flow to acquire evidence of independent object motion. L. Wixson notes that motion in the image does not always signify purposeful travel by an independently moving object (examples of such “motion clutter” are wind-blown tree branches and sun reflections off rippling water) and devises a flow-based salience measure to highlight objects that tend to move in a consistent direction over time. Human motion analysis is concerned with detecting periodic motion signifying a human gait and acquiring descriptions of human body pose over time. R. Cutler and L.S. Davis plot an object’s self-similarity across all pairs of frames to form distinctive patterns that classify bipedal, quadripedal, and rigid object motion. Y. Ricquebourg and P. Bouthemy track apparent contours in XT slices of an XYT sequence volume to robustly delineate and track articulated human body structure. I. Haritaoglu, D. Harwood, and L.S. Davis present W4, a surveillance system specialized to the task of looking at people. The W4 system can locate people and segment their body parts, build simple appearance models for tracking, disambiguate between and separately track multiple individuals in a group, and detect carried objects such as boxes and backpacks. Activity analysis deals with parsing temporal sequences of object observations to produce high-level descriptions of agent actions and multiagent interactions. In our opinion, this will be the most important area of future research in video surveillance. N.M. Oliver, B. Rosario, and A.P. Pentland introduce Coupled Hidden Markov Models (CHMMs) to detect and classify interactions consisting of two interleaved agent action streams and present a training method based on synthetic agents to address the problem of parameter estimation from limited real-world training examples. M. Brand and V. Kettnaker present an entropyminimization approach to estimating HMM topology and