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A traditional re-identification pipeline consists of a detection and re-identification step, i.e. a person detector is run on an input image to get a cutout which is then sent to a separate re-identification system. In this work we combine detection and re-identification into one single pass neural network. We propose an architecture that can do re-identification simultaneously with detection and classification. The effect of our modification has only a negligible impact on detection accuracy, and adds the calculation of re-identification vectors at virtually no cost. The resulting re-identification vector is strong enough to be used in speed sensitive applications which can benefit from an additional re-identification vector in addition to detection. We demonstrate this by using it as detection and re-identification input for a real-time person tracker. Moreover, unlike traditional detection + re-id pipelines our single-pass network’s computational cost is not dependent on the number of people in the image. ispartof: pages:49-54 ispartof: IEEE International Conference on Advanced Video and Signal-based Surveillance pages:49-54 ispartof: 15th IEEE International Conference on Advanced Video and Signal-based Surveillance location:Auckland, New Zealand date:27 Nov - 30 Nov 2018 status: published

Due to the wide applicability of pedestrian detection in surveillance and safety, this research topic has received much attention in computer vision literature. However, the focus of this research mainly lies in detecting and locating pedestrians individually as accurate as possible. In recent years, a number of datasets are captured using a forward looking camera from a car, which imposes the application of warning the driver when pedestrians are in front of the car. For such applications, it is not required to detect each pedestrian independently, but to generate an alarm when necessary. In this paper we explore techniques to boost the accuracy of recent channel-based algorithms in this application: algorithmic refinements as well as the inclusion of an LWIR image channel. We use the KAIST dataset which is constructed from image-pairs of both the visual and the LWIR spectrum, in day and night conditions. We study the influence of techniques that have shown success in literature.

Recent technical advances in Unmanned Aerial Vehicles (UAV) made a realm of applications possible. In this paper we focus on the application of following a walking pedestrian in real-time, using optimised pedestrian detection and object tracking. For this we use an on-board embedded system, offering an optimal ratio of computational power and weight. We extend the commonly used ground plane estimation technique, used to reduce the search space, based on the sensor data off the UAV. The integration of the ground plane constraint obtains a significant speed-up over the already optimised Aggregate Channel Feature (ACF) detector. To compensate for the frames without detections, we use a particle tracker based on color information. We successfully validated our system on a flying UAV. Geen ISBN ispartof: pages:1-8 ispartof: The IEEE Conference on Computer Vision and Pattern Recognition (CVPR) Workshops vol:2015-October pages:1-8 ispartof: Embedded Vision Workshop location:Boston, USA date:12 Jun - 12 Jun 2015 status: published

Accurate object detection has been studied thoroughly over the years. Although these techniques have become very precise, they lack the capability to cope with a rotated appearance of the object. In this paper we tackle this problem in a two step approach. First we train a specific model for each orientation we want to cover. Next to that we propose the use of a rotation map that contains the predicted orientation information at a specific location based on the dominant orientation. This helps us to reduce the number of models that will be evaluated at each location. Based on 3 datasets, we obtain a high speed-up while still maintaining accurate rotated object detection. ispartof: pages:400-407 ispartof: Proceedings of the 10th International Conference on Computer Vision Theory and Applications - (Volume 2) (VISIGRAPP 2015) vol:2 pages:400-407 ispartof: VISAPP location:Berlin, Germany date:11 Mar - 14 Mar 2015 status: published

In recent years, the accuracy of pedestrian detectors significantly improved. Currently, state-of-the-art pedestrian detectors achieve high accuracy results on challenging datasets. As opposed to refining a single detector, in this paper we propose a different approach to further increase the detection accuracy: combining multiple pedestrian detectors. The most straight-forward way to combine pedestrian detectors would be a naive AND or OR combination. Here, we present a novel generic combination framework in which we exploit specific information from each pedestrian detector to determine the optimal combination parameters. Our main motivation for this approach is based on the fact that several pedestrian detection approaches are based on very different techniques (e.g. a different feature pool), and thus an efficient combination should yield higher accuracy results. Indeed, such a combination is far more powerful, and our experiments indicate that specific (that is, cleverly chosen) combinations outperform existing state-of-the-art pedestrian detection results. De Smedt F., Van Beeck K., Tuytelaars T., Goedemé T., ''The combinator: optimal combination of multiple pedestrian detectors'', Proceedings 22nd international conference on pattern recognition - ICPR2014, pp. 3522-3527, August 24-28, 2014, Stockholm, Sweden. ispartof: pages:3522-3527 ispartof: Proceedings of the 22nd International Conference on Pattern Recognition pages:3522-3527 ispartof: International Conference on Pattern Recognition (ICPR) location:Stockholm, Sweden date:24 Aug - 28 Aug 2014 status: published

Object detection, and in particular pedestrian detection, is a challenging task, due to the wide variety of appearances. The application domain is extremely broad, ranging from e.g. surveillance to automotive safety systems. Many practical applications however often rely on stringent real-time processing speeds combined with high accuracy needs. These demands are contradictory, and usually a compromise needs to be made. In this paper we present a pedestrian detection framework which is extremely fast (500 detections per second) while still maintaining excellent accuracy results. We achieve these results by combining our fast pedestrian detection algorithm (implemented as a hybrid CPU and GPU combination) with the exploitation of scene constraints (using a warping window approach and temporal information), which yields state-of-the-art detection accuracy. We present profound evaluation results of our algorithm concerning both speed and accuracy on the challenging Caltech dataset. Furthermore we present evaluation results on a very specific application showing the full potential of this warping window approach: detection of pedestrians in a truck's blind spot zone. De Smedt F., Van Beeck K., Tuytelaars T., Goedemé T., ''Pedestrian detection at warp speed: exceeding 500 detections per second'', 9th IEEE embedded vision workshop - EVW2013, held in conjunction with IEEE CVPR 2013, pp. 622-628, June 24, 2013, Portland, Oregon, USA. ispartof: pages:622-628 ispartof: 2013 IEEE Conference on Computer Vision and Pattern Recognition Workshops CVPRW 2013 pages:622-628 ispartof: IEEE embedded vision workshop - EVW2013, held in conjunction with IEEE CVPR 2013 location:Portland, Oregon date:24 Jun - 24 Jun 2013 status: published