A modular tracking system for far infrared pedestrian recognition

Abstract

This paper describes a modular tracking system designed to improve the performance of a pedestrian detector. The tracking system consists of two modules, a kabeler and a predictor. The former associates a tracking identifier to each pedestrian, keeping memory of the past history; this is achieved by merging the detector and predictor outputs combined with data about vehicle motion. The predictor, basically a Kahan filter, estimates the new pedestrian position by observing hu previous movements. Its output helps the labeler to improve the match between the pedestrians detected in the new frame and those observed in the previous shots (feedback). If a pedestrian is occluded by some obstacle for a short while, the system continues tracking its movement using motion parameters. Moreover, it is able to reaSsign the same tracking ID in case the occlusion disappears in a short time. This behavior helps to correct temporary mis-recognitions that occur when the detector fails. The system has been tested using a quantitative performance evaluation tool, giving promising results.

Pedestrian Path Prediction with Recursive Bayesian Filters: A Comparative Study

“…Its applicability in real-time systems has been proven over many years for different sensors and application domains [1,3,4,18,21]. State parameters (e.g.…”

“…The KF can further be used for prediction by propagating the current state with the dynamical model without the inclusion of new measurements. Work by [3] on FIR-based pedestrian tracking uses a constant acceleration (CA) model in image space. Working in image space, however, makes it difficult to incorporate prior knowledge on the dynamics of pedestrian motion.…”

“…[3,5,9,15,18,21] do not include any such evaluation. [2] provides accuracy figures only related to its KF approach in indoor setting.…”

Pedestrian Detection in Poor Visibility Conditions: Would SWIR Help?

“…braking) and becomes particularly valuable in case of driver's distraction or poor visibility conditions. Yet visionbased pedestrian detection is a difficult problem for a number of reasons [2]. The objects of interest appear in highly cluttered backgrounds and have a wide range of appearances, due to body size and pose, clothing and outdoor lighting conditions.…”

Discrete features for rapid pedestrian detection in infrared images

“…Most systems take advantage of this characteristic and select the regions of interest based on the distribution of warm areas on the image [2] [3] [4].On systems that search for the temperature distribution, the discriminating feature of pedestrians would be the shape of the object. Regions of interest are correlated with some predefined probabilistic models in [5] and [6].…”