Abstract book (pdf) - ICPR 2010

09:40-10:00, Paper ThAT3.3
Crowd Motion Analysis using Linear Cyclic Pursuit
Viswanathan, Srikrishnan, I.I.T Bombay
Chaudhuri, Subhasis, IIT
Crowd motion analysis, where there is interdependence amongst the constituent elements, is a relatively unexplored application
area in computer vision. In this work, we propose a fast method for short-term crowd motion prediction using a
sparse set of particles. We study the dynamics of a crowd motion model and linear cyclic pursuit. We show that linear
cyclic pursuit naturally captures the repulsive and attractive forces acting on the individual crowd member. The pursuit
parameters are estimated from videos in an online manner using a feature tracker. Short term trajectory prediction is done
by numerical solution of estimated cyclic pursuit equation. We demonstrate the suitability of the proposed technique
through extensive experimentations.
10:00-10:20, Paper ThAT3.4
Integrating Object Detection with 3D Tracking towards a Better Driver Assistance System
Prisacariu, Victor Adrian, Univ. of Oxford
Timofte, Radu, Katholieke Univ. Leuven
Zimmermann, Karel, Katholieke Univ. Leuven
Reid, Ian,
Van Gool, Luc
Driver assistance helps save lives. Accurate 3D pose is required to establish if a traffic sign is relevant to the driver. We
propose a real-time system that integrates single view detection with region-based 3D tracking of road signs. The optimal
set of candidate detections is found, followed by AdaBoost cascades and SVMs. The 2D detections are then employed in
simultaneous 2D segmentation and 3D pose tracking, using the known 3D model of the recognised traffic sign. We demonstrate
the abilities of our system by tracking multiple road signs in real world scenarios.
10:20-10:40, Paper ThAT3.5
Real-Time Automatic Traffic Accident Recognition using HFG
Bakheet, Samy, Otto-von-Guericke Univ. Magdeburg
Al-Hamadi, Ayoub, Otto-von-Guericke Univ. Magdeburg
Michaelis, Bernd, Otto-von-Guericke Univ. Magdeburg
Sayed, Usama, Otto-von-Guericke Univ. Magdeburg
Recently, the problem of automatic traffic accident recognition has appealed to the machine vision community due to its
implications on the development of autonomous Intelligent Transportation Systems (ITS). In this paper, a new framework
for real-time automated traffic accidents recognition using Histogram of Flow Gradient (HFG) is proposed. This framework
performs two major steps. First, HFG-based features are extracted from video shots. Second, logistic regression is employed
to develop a model for the probability of occurrence of an accident by fitting data to a logistic curve. In case of occurrence
of an accident, the trajectory of vehicle by which the accident was occasioned is determined. Preliminary results on real
video sequences confirm the effectiveness and the applicability of the proposed approach, and it can offer delay guarantees
for real-time surveillance and monitoring scenarios.
ThAT4 Dolmabahçe Hall A
Semi-Supervised and Metric Learning Regular Session
Session chair: Sanfeliu, Alberto (Universitat Politecnica de Catalunya)
09:00-09:20, Paper ThAT4.1
Semi-Supervised Distance Metric Learning by Quadratic Programming
Cevikalp, Hakan, Eskisehir Osmangazi Univ.
This paper introduces a semi-supervised distance metric learning algorithm which uses pair-wise equivalence (similarity
and dissimilarity) constraints to improve the original distance metric in lower-dimensional input spaces. We restrict ourselves
to pseudo-metrics that are in quadratic forms parameterized by positive semi-definite matrices. The proposed method
works in both the input space and kernel in-duced feature space, and learning distance metric is formulated as a quadratic
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