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      Looking at Humans in the Age of Self-Driving and Highly Automated Vehicles

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          Vision meets robotics: The KITTI dataset

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            Are we ready for autonomous driving? The KITTI vision benchmark suite

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              Monocular pedestrian detection: survey and experiments.

              Pedestrian detection is a rapidly evolving area in computer vision with key applications in intelligent vehicles, surveillance, and advanced robotics. The objective of this paper is to provide an overview of the current state of the art from both methodological and experimental perspectives. The first part of the paper consists of a survey. We cover the main components of a pedestrian detection system and the underlying models. The second (and larger) part of the paper contains a corresponding experimental study. We consider a diverse set of state-of-the-art systems: wavelet-based AdaBoost cascade [74], HOG/linSVM [11], NN/LRF [75], and combined shape-texture detection [23]. Experiments are performed on an extensive data set captured onboard a vehicle driving through urban environment. The data set includes many thousands of training samples as well as a 27-minute test sequence involving more than 20,000 images with annotated pedestrian locations. We consider a generic evaluation setting and one specific to pedestrian detection onboard a vehicle. Results indicate a clear advantage of HOG/linSVM at higher image resolutions and lower processing speeds, and a superiority of the wavelet-based AdaBoost cascade approach at lower image resolutions and (near) real-time processing speeds. The data set (8.5 GB) is made public for benchmarking purposes.
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                Author and article information

                Journal
                IEEE Transactions on Intelligent Vehicles
                IEEE Trans. Intell. Veh.
                Institute of Electrical and Electronics Engineers (IEEE)
                2379-8904
                2379-8858
                March 2016
                March 2016
                : 1
                : 1
                : 90-104
                Article
                10.1109/TIV.2016.2571067
                89354f1b-7db9-4c5c-8d09-0e322e00df4b
                © 2016
                Product
                Self URI (article page): http://ieeexplore.ieee.org/document/7501845/

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