Abstract book (pdf) - ICPR 2010

13:30-16:30, Paper TuBCT8.24
Discriminative Level Set for Contour Tracking
Li, Wei, Chinese Acad. of Sciences
Conventional contour tracking algorithms with level set often use generative models to construct the energy function. For
tracking through cluttered and noisy background, however, a generative model may not be discriminative enough. In this
paper we integrate the discriminative methods into a level set framework when constructing the level set energy function.
We train a set of weak classifiers to distinguish the object from the background. Each weak classifier is designed to select
the most discriminative feature space and integrated via AdaBoost according to their training errors. We also introduce a
novel interaction term to explore the correlation between pixels near the object edge. This term together with the discriminative
model both enhance the discriminative power of the level set. The experimental results show that the contour
tracked by our approach is more accurate than the conventional algorithms with the generative model. Our algorithm successfully
tracks the object contour even in a cluttered environment.
13:30-16:30, Paper TuBCT8.25
Tracking Objects with Adaptive Feature Patches for PTZ Camera Visual Surveillance
Xie, Yi, Beijing Inst. of Tech.
Lin, Liang, Lotushill Inc
Jia, Yunde, Beijing Inst. of Tech.
Compared to the traditional tracking with fixed cameras, the PTZ-camera-based tracking is more challenging due to (I)
lacking of reliable background modeling and subtraction; (ii) the appearance and scale of target changing suddenly and
drastically. Tackling these problems, this paper proposes a novel tracking algorithm using patch-based object models and
demonstrates its advantages with the PTZ-camera in the application of visual surveillance. In our method, the target model
is learned and represented by a set of feature patches whose discriminative power is higher than others. The target model
is matched and evaluated by both appearance and motion consistency measurements. The homography between frames is
also calculated for scale adaptation. The experiment on several surveillance videos shows that our method outperforms
the state-of-arts approaches.
13:30-16:30, Paper TuBCT8.26
Counting Moving People in Videos by Salient Points Detection
Conte, Donatello, Univ. di Salerno
Foggia, Pasquale, Univ. di Salerno
Percannella, Gennaro, Univ. di Salerno
Tufano, Francesco, Univ. degli Studi di Salerno
Vento, Mario, Univ. degli Studi di Salerno
This paper presents a novel method to count people for video surveillance applications. The problem is faced by establishing
a mapping between some scene features and the number of people. Moreover, the proposed technique takes specifically
into account problems due to perspective. In the experimental evaluation, the method has been compared with respect to
the algorithm by Albiol et al., which provided the highest performance at the PETS 2009 contest on people counting, using
the same datasets. The results confirm that the proposed method improves the accuracy, while retaining the robustness of
Albiol’s algorithm.
13:30-16:30, Paper TuBCT8.27
Visualization of Customer Flow in an Office Complex over a Long Period
Onishi, Masaki, National Inst. of Advanced Industrial Science and Technology
Yoda, Ikushi, National Inst. of Advanced Industrial Science and Technology
In facility management, analysis of customer trajectories in office complexes is considered critical. In this paper, we
propose a novel approach for the visualization of customer flow in an office complex over a long period of time. We expressed
the variation in the trajectories with respect to time by using a mixture model; this was used for the visualization
of the trajectory flows. The effectiveness of our approach was evaluated from the results of the customer flow analysis experiments
that were conducted in an office complex.
- 135 -