Abstract book (pdf) - ICPR 2010

WeAT1 Marmara Hall
Tracking and Surveillance - II Regular Session
Session chair: Yilmaz, Alper (The Ohio State Univ.)
09:00-09:20, Paper WeAT1.1
The Fusion of Deep Learning Architectures and Particle Filtering Applied to Lip Tracking
Carneiro, Gustavo, Tech. Univ. of Lisbon
Nascimento, Jacinto, Inst. de Sistemas e Robótica
This work introduces a new pattern recognition model for segmenting and tracking lip contours in video sequences. We
formulate the problem as a general nonrigid object tracking method, where the computation of the expected segmentation
is based on a filtering distribution. This is a difficult task because one has to compute the expected value using the whole
parameter space of segmentation. As a result, we compute the expected segmentation using sequential Monte Carlo sampling
methods, where the filtering distribution is approximated with a proposal distribution to be used for sampling. The
key contribution of this paper is the formulation of this proposal distribution using a new observation model based on
deep belief networks and a new transition model. The efficacy of the model is demonstrated in publicly available databases
of video sequences of people talking and singing. Our method produces results comparable to state-of-the-art models, but
showing potential to be more robust to imaging conditions.
09:20-09:40, Paper WeAT1.2
Robust Head-Shoulder Detection by PCA-Based Multilevel HOG-LBP Detector for People Counting
Zeng, Chengbin, Beijing Univ. of Posts and Telecommunications
Ma, Huadong, Beijing Univ. of Posts and Telecommunications
Robustly counting the number of people for surveillance systems has widespread applications. In this paper, we propose
a robust and rapid head-shoulder detector for people counting. By combining the multilevel HOG (Histograms of Oriented
Gradients) with the multilevel LBP (Local Binary Pattern) as the feature set, we can detect the head-shoulders of people
robustly, even though there are partial occlusions occurred. To further improve the detection performance, Principal Components
Analysis (PCA) is used to reduce the dimension of the multilevel HOG-LBP feature set. Our experiments show
that the PCA based multilevel HOG-LBP descriptors are more discriminative, more robust than the state-of-the-art algorithms.
For the application of the real-time people-flow estimation, we also incorporate our detector into the particle filter
tracking and achieve convincing accuracy
09:40-10:00, Paper WeAT1.3
Adaptive Motion Model for Human Tracking using Particle Filter
Mohammad Hossein Ghaeminia, Mohammad Hossein Ghaeminia, Iran Univ. of Science and Tech.
Shabani, Amir-Hossein, Univ. of Waterloo
Baradaran Shokouhi, Shahriar, Iran Univ. ofScience & Tech.
This paper presents a novel approach to model the complex motion of human using a probabilistic autoregressive moving
average model. The parameters of the model are adaptively tuned during the course of tracking by utilizing the main
varying components of the pdf of the target’s acceleration and velocity. This motion model, along with the color histogram
as the measurement model, has been incorporated in the particle filtering framework for human tracking. The proposed
method is evaluated by PETS benchmark in which the targets have non-smooth motion and suddenly change their motion
direction. Our method competes with the state-of-the-art techniques for human tracking in the real world scenario.
10:00-10:20, Paper WeAT1.4
Bayesian GOETHE Tracking
Wirkert, Sebastian, Ec. Centrale de Lyon
Dellandréa, Emmanuel, Ec. Centrale de Lyon
Chen, Liming, Ec. Centrale de Lyon
Occlusions pose serious challenges when tracking multiple targets. By severly changing the measurement, they imply
strong inter-target dependencies. Exact computation of these dependencies is not feasible. The GOETHE approximations
preserve much of the information while staying computationally affordable.
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