Procedings of the British Machine Vision Conference 2015 2015
DOI: 10.5244/c.29.32
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Real-Time Pedestrian Detection with Deep Network Cascades

Abstract: Pedestrian detection has been an important problem for decades, given its relevance to a number of applications in robotics, including driver assistance systems, road scene understanding and surveillance systems. The two main practical requirements for fielding such systems are very high accuracy and real-time speed: we need pedestrian detectors that are accurate enough to be relied on and are fast enough to run on systems with limited compute power. This paper addresses both of these requirements by combining… Show more

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Cited by 189 publications
(118 citation statements)
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References 36 publications
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“…Chai and Hodgins obtain sufficient quality to drive virtual avatars in real-time, but require visual markers [Chai and Hodgins 2005]. The use of CNNs in real time has been explored for variants of the object detection problem, for instance bounding box detection and pedestrian detection methods have leveraged application specific architectures [Angelova et al 2015;Liu et al 2016;Redmon et al 2015] and preprocessing steps [Ren et al 2015].…”
Section: Multi-viewmentioning
confidence: 99%
“…Chai and Hodgins obtain sufficient quality to drive virtual avatars in real-time, but require visual markers [Chai and Hodgins 2005]. The use of CNNs in real time has been explored for variants of the object detection problem, for instance bounding box detection and pedestrian detection methods have leveraged application specific architectures [Angelova et al 2015;Liu et al 2016;Redmon et al 2015] and preprocessing steps [Ren et al 2015].…”
Section: Multi-viewmentioning
confidence: 99%
“…The conclusion of this work is that CNN can provide acceptable solution for the above tasks by running at frame rate which is required for a real-time system. In another recent work, authors use deep learning for detecting pedestrians [36], however, authors comments that although the performance of deep network is better than cascade algorithms for detecting complex patterns, it is slow for a real-time pedestrian detection. None of the existing works consider sensor fusion along with deep neutral network, which is the focus of our work.…”
Section: Preliminary Resultsmentioning
confidence: 99%
“…[21][22][23][24][25][26][27][28][29][30][31][32][33][34][35][36][37][38][39] Here, we review the methods based on the Hough transform framework 1,2,4-68-10 that are most relevant to our work.…”
Section: Hough Transform Methodsmentioning
confidence: 99%