Abstract book (pdf) - ICPR 2010

09:00-11:10, Paper ThAT8.53
Efficient 3D Upper Body Tracking with Self-Occlusions
Chen, Jixu, RPI
Ji, Qiang, RPI
We propose an efficient 3D upper body tracking method, which recovers the positions and orientations of six upper-body
parts from the video sequence. Our method is based on a probabilistic graphical model (PGM), which incorporates the
spatial relationships among the body parts, and a robust multi-view image likelihood using probabilistic PCA (PPCA).
For the efficiency, we use a tree-structured graphical model and use the particle based belief propagation to perform the
inference. Since our image likelihood is based on multiple views, we address the self-occlusion by modeling the likelihood
of the body part in each view, and automatically decrease the influence of the occluded view in the inference procedure.
09:00-11:10, Paper ThAT8.54
Track Initialization in Low Frame Rate and Low Resolution Videos
Cuntoor, Naresh, Kitware Inc.
Basharat, Arslan, Kitware Inc.
Perera, A. G. Amitha, Kitware Inc.
Hoogs, Anthony, Kitware Inc.
The problem of object detection and tracking has received relatively less attention in low frame rate and low resolution
videos. Here we focus on motion segmentation in videos where objects appear small (less than 30-pixel tall people) and
have low frame rate (less than 5 Hz). We study challenging cases where some of the, otherwise successful, approaches
may break down. We investigate a number of popular techniques in computer vision that have been shown to be useful
for discriminating various spatio-temporal signatures. These include: Histogram of oriented Gradients (HOG), Histogram
of oriented optical Flow (HOF) and Haar-features (Viola and Jones). We use these feature to classify the motion segmentations
into person vs. other and vehicle vs. other. We rely on aligned motion history images to create a more consistent
object representation across frames. We present results on these features using webcam data and wide-area aerial video
sequences.
09:00-11:10, Paper ThAT8.55
On the Performance of Handoff and Tracking in a Camera Network
Li, Yiming, Univ. of California Riverside
Bhanu, Bir, Univ. of California Riverside
Nguyen, Vincent, Univ. of California Riverside
Camera handoff is an important problem when using multiple cameras to follow a number of objects in a video network.
However, almost all the handoff techniques rely on a robust tracker. State-of-the-art techniques used to evaluate the performance
of camera handoff use either annotated videos or simulated data, and the handoff performance is evaluated in
conjunction with a tracker. This does not allow a deeper understanding into the performance of a tracker and a handoff
technique separately in the real-world settings. In this paper, we evaluate three camera handoff techniques, two different
color-based trackers in seven real-life cases, with varying numbers of cameras, number of objects and the changing environmental
conditions. We also perform experiments on annotated videos to provide the ground-truth for all the scenarios.
This evaluation of performance isolates the effect of tracking and handoff techniques and clarifies their role in a video
network.
09:00-11:10, Paper ThAT8.56
Object Tracking with Ratio Cycles using Shape and Appearance Cues
Sargin, Mehmet Emre, UC Santa Barbara
Ghosh, Pratim, UC Santa Barbara
Manjunath, B. S., UC Santa Barbara
Rose, Kenneth, UC Santa Barbara
We present a method for object tracking over time sequence imagery. The image plane is represented with a 4-connected
planar graph where vertices are associated with pixels. On each image, the outer contour of the object is localized by
finding the optimal cycle in the graph such that a cost function based on temporal, appearance and shape priors is minimized.
Our contribution is the particle filtering-based framework to integrate the shape cue with the temporal and appear-
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