Large-scale vehicle detection in challenging urban surveillance environments

Feris, Rogério; Petterson, James; Siddiquie, Behjat; Brown, Lisa M.; Pankanti, Sharath

doi:10.1109/wacv.2011.5711549

Cited by 25 publications

(14 citation statements)

References 13 publications

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“…Feris et al [5] proposed a technique to automatically collect training data from the target domain and learn a classifier. However, they require user input to specify regionsof-interest and attributes such as motion direction and acceptable aspect ratios of the object of interest.…”

Section: Related Workmentioning

confidence: 99%

Boosting object detection performance in crowded surveillance videos

Feris

Datta

Pankanti

et al. 2013

2013 IEEE Workshop on Applications of Computer Vision (WACV)

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We present a novel approach to automatically create efficient and accurate object detectors tailored to work well on specific video surveillance cameras (specific-domain detectors), using samples acquired with the help of a more expensive, general-domain detector (trained using images from multiple cameras). Our method requires no manual labels from the target domain. We automatically collect training data using tracking over short periods of time from high-confidence samples selected by the general-domain detector. In this context, a novel confidence measure is proposed for detectors based on a cascade of classifiers, which are frequently adopted for computer vision applications that require real-time processing. We demonstrate our proposed approach on the problem of vehicle detection in crowded surveillance videos, showing that an automatically generated detector significantly outperforms the original general-domain detector with much less feature computations.

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Section: Related Workmentioning

confidence: 99%

Boosting object detection performance in crowded surveillance videos

Feris

Datta

Pankanti

et al. 2013

2013 IEEE Workshop on Applications of Computer Vision (WACV)

Self Cite

View full text Add to dashboard Cite

show abstract

“…To support the vehicle-search feature, we instead have implemented a novel multiview detection system that relies on a set of Bmotion-let[ classifiers. This consists of a bank of trained detectors using vehicle samples clustered in various parts of the motion configuration space [3]. We learn each detector by using massively parallel feature selection of local image patch descriptors.…”

Section: Video Surveillancementioning

confidence: 99%

Practical computer vision: Example techniques and challenges

et al. 2011

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“…Vision-based vehicle detection systems have been used for various applications such as intelligent vehicles, driver assistance systems [1], [2], autonomous vehicles [3]- [5], traffic monitoring systems [6]- [8], traffic-signal control systems [9], [10], and so on.…”

Section: Introductionmentioning

confidence: 99%

State Machine and Downhill Simplex Approach for Vision-Based Nighttime Vehicle Detection

Choi

Kim

et al. 2014

ETRI J

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In this paper, a novel vision-based nighttime vehicle detection approach is presented, combining state machines and downhill simplex optimization. In the proposed approach, vehicle detection is modeled as a sequential state transition problem; that is, vehicle arrival, moving, and departure at a chosen detection area. More specifically, the number of bright pixels and their differences, in a chosen area of interest, are calculated and fed into the proposed state machine to detect vehicles. After a vehicle is detected, the location of the headlights is determined using the downhill simplex method. In the proposed optimization process, various headlights were evaluated for possible headlight positions on the detected vehicles; allowing for an optimal headlight position to be located. Simulation results were provided to show the robustness of the proposed approach for nighttime vehicle and headlight detection.

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Large-scale vehicle detection in challenging urban surveillance environments

Cited by 25 publications

References 13 publications

Boosting object detection performance in crowded surveillance videos

Boosting object detection performance in crowded surveillance videos

Practical computer vision: Example techniques and challenges

State Machine and Downhill Simplex Approach for Vision-Based Nighttime Vehicle Detection

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