Publication

Multi-layer hierarchical clustering of pedestrian trajectories for automatic counting of people in video sequences

Jean-Philippe Thiran
2005
Conference Papers

Abstract

In this paper we propose an approach to count the number of pedestrians, given a trajectory data set provided by a tracking system. The tracking process itself is treated as a black box providing us the input data. The idea is to apply a hierarchical clustering algorithm, using different data representations and distance measures, as a post-processing step. The final goal is to reduce the difference between the number of tracked pedestrians and the real number of individuals present in the scene.

Official source

https://infoscience.epfl.ch/entities/publication/a796d5d6-e74a-47ea-8cff-c00713d4ab51

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Jean-Philippe Thiran
2005
Conference Papers

Abstract

Official source

https://infoscience.epfl.ch/entities/publication/a796d5d6-e74a-47ea-8cff-c00713d4ab51

About this result

Related concepts (32)

Tracking system

A tracking system, also known as a locating system, is used for the observing of persons or objects on the move and supplying a timely ordered sequence of location data for further processing. A myriad of tracking systems exists. Some are 'lag time' indicators, that is, the data is collected after an item has passed a point for example a bar code or choke point or gate. Others are 'real-time' or 'near real-time' like Global Positioning Systems (GPS) depending on how often the data is refreshed.

Hierarchical clustering

In data mining and statistics, hierarchical clustering (also called hierarchical cluster analysis or HCA) is a method of cluster analysis that seeks to build a hierarchy of clusters. Strategies for hierarchical clustering generally fall into two categories: Agglomerative: This is a "bottom-up" approach: Each observation starts in its own cluster, and pairs of clusters are merged as one moves up the hierarchy. Divisive: This is a "top-down" approach: All observations start in one cluster, and splits are performed recursively as one moves down the hierarchy.

Cluster analysis

Cluster analysis or clustering is the task of grouping a set of objects in such a way that objects in the same group (called a cluster) are more similar (in some sense) to each other than to those in other groups (clusters). It is a main task of exploratory data analysis, and a common technique for statistical data analysis, used in many fields, including pattern recognition, , information retrieval, bioinformatics, data compression, computer graphics and machine learning.

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Multi-layer hierarchical clustering of pedestrian trajectories for automatic counting of people in video sequences

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