A One-and-Half Stage Pedestrian Detector

International audiencePedestrian detection is a specific instance of the more general problem of object detection in computer vision. A balance between detection accuracy and speed is a desirable trait for pedestrian detection systems in many applications such as self-driving cars. In this paper, we follow the wisdom of " and less is often more" to achieve this balance. We propose a lightweight mechanism based on semantic segmentation to reduce the number of anchors to be processed. We furthermore unify this selection with the intra-anchor feature pooling strategy adopted in high performance two-stage detectors such as Faster-RCNN. Such a strategy is avoided in one-stage detectors like SSD in favour of faster inference but at the cost of reducing the accuracy vis-à-vis two-stage detectors. However our anchor selection renders it practical to use feature pooling without giving up the inference speed. Our proposed approach succeeds in detecting pedestrians with state-of-art performance on caltech-reasonable and ciypersons datasets with inference speeds of ∼ 32 fps

Trials with Microwave Detection of Vulnerable Road Users and Preliminary Empirical Modal Test. DRIVE Project V1031 Deliverable 11.

The general objective of the project is to provide a set of tools for the creation of traffic systems that enhance the safety and mobility of vulnerable road users (VRUs). This is being achieved in two ways: 1. By evaluating a number of RTI applications in signalling and junction control, in order to ascertain what benefits can be obtained for vulnerable road users by such local measures. 2. By developing a model of the traffic system that incorporates vulnerable road users as an integral part. The present workpackage, one of the last ones within the project, is intended to link the two strands together. The workpackage consists of two main parts: 1. Experiments with pedestrians and bicyclists. Two experiments were carried out, one in England (Bradford) and one in Sweden (Vijrjo), both applying microwave detectors for detection of pedestrians in a signalized intersection, but applying the detection in different ways. An observational study was carried out in Groningen (the Netherlands) to analyze bicycle/car interactions at an intersection with a cycle path. The aim of the experiment was to test the usefulness of a system giving car drivers warning in situations when a bicyclist approaches an intersection on a parallel bicycle path. 2. Reliability and validity testing of the submodels of the VRU-oriented traffic model WLCAN

Assessment of the Effectiveness of the Greek Implementation. VRU-TOO Deliverable 14

The work of VRU-TOO is targeted specifically at the application of ATT for reducing risk and improving comfort (e.g. minimisation of delay) for Vulnerable Road Users, namely pedestrians. To achieve this, the project operates at three levels. At the European level practical pilot implementations in three countries (U.K., Portugal and Greece) are linked with behavioural studies of the micro-level interaction of pedestrians and vehicles and the development of computer simulation models. At the National level, the appropriate Highway Authorities are consulted, according to their functions, for the pilot implementations and informed of the results. Finally, at the local level, the pilot project work is fitted into specfic local (municipality) policy contexts in all three pilot project sites. The present report focuses on the Elefsina pilot application in Greece and the relevant National and Local policy contexts are the following. At the National level, the ultimate responsibility for road safety and signal installations rests with the Ministry of Environment and Public Works. The Ministry is responsible for the adoption of standards and solutions for problems and also for a large number of actual installations, since local authorities lack the size and expertise to undertake such work on their own One of the project's aims is to provide information to the Ministry as to the suitability of the methods developed for aiding pedestrian movement, ultimately leading to a specification for its wider use. The Ministry is expecting to use the final results of the present study for possible modifications of its present standards for pedestrian controlled traffic signals. At the local level (Elefsina) the municipality has, in the past, pursued environmental improvements through pedestrianisation schemes in the city centre. At the same time it has developed a special traffic management policy, to solve a particularly serious problem of through traffic. A summary of the policy is contained in Appendix A and more details in a previous deliverable (Tillis, 1992). In the particular case of Elefsina pedestrian induced delay to through vehicular traffic, may form a key element in this policy ensuring at the same time, an incentive to divert to the existing bypass and enhancing pedestrian movement. The effectiveness of pedestrian detection techniques tested in the pilot, will provide valuable information on the future implementation of the policy. Thus, the Elefsina Pilot Project operates at the same time on three levels: It provides a basis, in combination with the other two pilot project sites, for comparing the effects of pedestrian detection on pedestrian safety and comfort at a European level. It provides information to the National authorities (Ministry of Environment and Public Works) for their standards setting, scheme design and implementation tasks. It fits into a comprehensive plan at the local level for effecting environmental improvements and enhancing pedestrian amenity and comfort at the same time. In addition, an investigation into the capabilities of pedestrian detectors to function as data collection devices, was performed. The data 'quality gap' betweenvehicular and pedestrian tr&c may be closed with the utilisation of microwave pedestrian detectors, providing a more solid foundation for the planning for total person movement through networks (vehicle occupants, public transport passengers, pedestrians). This the second deliverable issued for Elefsina and comprises of the main section which contains a description of the work undertaken, the results and a number of appendices serving as background material in support of the statements in the main text

Fusion of Multispectral Data Through Illumination-aware Deep Neural Networks for Pedestrian Detection

Multispectral pedestrian detection has received extensive attention in recent years as a promising solution to facilitate robust human target detection for around-the-clock applications (e.g. security surveillance and autonomous driving). In this paper, we demonstrate illumination information encoded in multispectral images can be utilized to significantly boost performance of pedestrian detection. A novel illumination-aware weighting mechanism is present to accurately depict illumination condition of a scene. Such illumination information is incorporated into two-stream deep convolutional neural networks to learn multispectral human-related features under different illumination conditions (daytime and nighttime). Moreover, we utilized illumination information together with multispectral data to generate more accurate semantic segmentation which are used to boost pedestrian detection accuracy. Putting all of the pieces together, we present a powerful framework for multispectral pedestrian detection based on multi-task learning of illumination-aware pedestrian detection and semantic segmentation. Our proposed method is trained end-to-end using a well-designed multi-task loss function and outperforms state-of-the-art approaches on KAIST multispectral pedestrian dataset

Joint 3D Proposal Generation and Object Detection from View Aggregation

We present AVOD, an Aggregate View Object Detection network for autonomous driving scenarios. The proposed neural network architecture uses LIDAR point clouds and RGB images to generate features that are shared by two subnetworks: a region proposal network (RPN) and a second stage detector network. The proposed RPN uses a novel architecture capable of performing multimodal feature fusion on high resolution feature maps to generate reliable 3D object proposals for multiple object classes in road scenes. Using these proposals, the second stage detection network performs accurate oriented 3D bounding box regression and category classification to predict the extents, orientation, and classification of objects in 3D space. Our proposed architecture is shown to produce state of the art results on the KITTI 3D object detection benchmark while running in real time with a low memory footprint, making it a suitable candidate for deployment on autonomous vehicles. Code is at: https://github.com/kujason/avodComment: For any inquiries contact aharakeh(at)uwaterloo(dot)c