Publication

BEHAVIORAL FILTERING OF HUMAN TRAJECTORIES FOR AUTOMATIC-MULTI-TRACK INITIATION

Michel Bierlaire, Jean-Philippe Thiran
2004
Reports

Abstract

In this paper we describe a new approach for the multitrack initiation problem. We propose an extensive use of the top-view reconstruction of the scene to solve the detection step in tracking pedestrians. We leave a large set of starting hypothetical moving objects free to evolve in the scene for a certain number of frames. The number of trajectories is pre-filtered using distance and direction constraints on a one-step movement of pedestrians. The output trajectories from pre-filtering step are then filtered using a discrete choice model for pedestrian behavior, calibrated on real data. The results show that is possible to use this technique to perform multitarget tracking in real situations. We particularly focus on an application in the context of automatic video surveillance.

Official source

https://infoscience.epfl.ch/entities/publication/813f5df6-c3b1-4fe8-a2a3-7ee02aa7b3f9

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Michel Bierlaire, Jean-Philippe Thiran
2004
Reports

Abstract

Official source

https://infoscience.epfl.ch/entities/publication/813f5df6-c3b1-4fe8-a2a3-7ee02aa7b3f9

About this result

Related concepts (32)

Low-pass filter

A low-pass filter is a filter that passes signals with a frequency lower than a selected cutoff frequency and attenuates signals with frequencies higher than the cutoff frequency. The exact frequency response of the filter depends on the filter design. The filter is sometimes called a high-cut filter, or treble-cut filter in audio applications. A low-pass filter is the complement of a high-pass filter. In optics, high-pass and low-pass may have different meanings, depending on whether referring to the frequency or wavelength of light, since these variables are inversely related.

Kalman filter

For statistics and control theory, Kalman filtering, also known as linear quadratic estimation (LQE), is an algorithm that uses a series of measurements observed over time, including statistical noise and other inaccuracies, and produces estimates of unknown variables that tend to be more accurate than those based on a single measurement alone, by estimating a joint probability distribution over the variables for each timeframe. The filter is named after Rudolf E. Kálmán, who was one of the primary developers of its theory.

Median filter

The median filter is a non-linear digital filtering technique, often used to remove noise from an image or signal. Such noise reduction is a typical pre-processing step to improve the results of later processing (for example, edge detection on an image). Median filtering is very widely used in digital because, under certain conditions, it preserves edges while removing noise (but see the discussion below), also having applications in signal processing.

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BEHAVIORAL FILTERING OF HUMAN TRAJECTORIES FOR AUTOMATIC-MULTI-TRACK INITIATION

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