A Novel Algorithm for Night Time Vehicle Detection Even with One Non-functional Taillight by CIOF (Color Inherited Optical Flow)

Dave, Priya; Gella, N.M.; Saboo, Nihil; Das, Arshavee

doi:10.1049/ic.2016.0031

Cited by 6 publications

(3 citation statements)

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“…Haar-like features over gray-level images are computed, which are then delivered to a modified version of the Adaboost classifier [21] to allow active learning. This is also the basis of the work by Dave et al [22], focused on recognizing just one of the vehicle rear lamps. For this purpose, a color segmentation algorithm is applied to detect bright blobs in the HSV color space.…”

Section: Introductionmentioning

confidence: 86%

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A Novel System for Nighttime Vehicle Detection Based on Foveal Classifiers With Real-Time Performance

Bell

Mantecón

Díaz

et al. 2022

IEEE Trans. Intell. Transport. Syst.

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Section: Introductionmentioning

confidence: 86%

“…For this purpose, a color segmentation algorithm is applied to detect bright blobs in the HSV color space. Then, the Color Inherited Optical Flow (CIOF) algorithm [23] is used to track the blobs. In [24], vehicle detection is performed by detecting the brake light.…”

Section: Introductionmentioning

confidence: 99%

A Novel System for Nighttime Vehicle Detection Based on Foveal Classifiers With Real-Time Performance

Bell

Mantecón

Díaz

et al. 2022

IEEE Trans. Intell. Transport. Syst.

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“…However, generating a displacement vector for each pixel is time consuming. In contrast to pixel-based optical flow, feature based methods, such as color features [27], extract features from an image. And then optical flow of feature points will be computed.…”

Section: A Vehicle Detectionmentioning

confidence: 99%

Tracking as a Whole: Multi-Target Tracking by Modeling Group Behavior With Sequential Detection

Yuan

Wang

2017

IEEE Trans. Intell. Transport. Syst.

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Video-based vehicle detection and tracking is one of the most important components for Intelligent Transportation Systems (ITS). When it comes to road junctions, the problem becomes even more difficult due to the occlusions and complex interactions among vehicles. In order to get a precise detection and tracking result, in this work we propose a novel trackingby-detection framework. In the detection stage, we present a sequential detection model to deal with serious occlusions. In the tracking stage, we model group behavior to treat complex interactions with overlaps and ambiguities. The main contributions of this paper are twofold: 1) Shape prior is exploited in the sequential detection model to tackle occlusions in crowded scene. 2) Traffic force is defined in the traffic scene to model group behavior, and it can assist to handle complex interactions among vehicles. We evaluate the proposed approach on real surveillance videos at road junctions and the performance has demonstrated the effectiveness of our method.

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Driver Assistance System for Recognition of Brake and Parking Signal

Bhatlawande¹,

Mhamane²,

Pande³

et al. 2023

Lecture Notes in Networks and Systems

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A Novel Algorithm for Night Time Vehicle Detection Even with One Non-functional Taillight by CIOF (Color Inherited Optical Flow)

Cited by 6 publications

References 0 publications

A Novel System for Nighttime Vehicle Detection Based on Foveal Classifiers With Real-Time Performance

A Novel System for Nighttime Vehicle Detection Based on Foveal Classifiers With Real-Time Performance

Tracking as a Whole: Multi-Target Tracking by Modeling Group Behavior With Sequential Detection

Driver Assistance System for Recognition of Brake and Parking Signal

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