Front and Rear Vehicle Detection Using Hypothesis Generation and Verification

Khairdoost, Nima; S, Amirhassan Monadjemi; Jamshidi, Kamal

doi:10.5121/sipij.2013.4403

Cited by 12 publications

(2 citation statements)

References 56 publications

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“…It initially achieved significant success in pedestrian detection [54], and has since expanded to other application domains, such as vehicle detection and face recognition. Many researchers have improved upon the HOG algorithm, such as two HOG vectors [55], the pyramid of HOG [56], and symmetry HOG [57]. The deformable part model (DPM) employs the improved HOG descriptor and adopts a multi-component strategy [58].…”

Section: Feature Extractionmentioning

confidence: 99%

Vehicle Detection Algorithms for Autonomous Driving: A Review

Liang,

Ma,

Zhao

et al. 2024

Sensors

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Autonomous driving, as a pivotal technology in modern transportation, is progressively transforming the modalities of human mobility. In this domain, vehicle detection is a significant research direction that involves the intersection of multiple disciplines, including sensor technology and computer vision. In recent years, many excellent vehicle detection methods have been reported, but few studies have focused on summarizing and analyzing these algorithms. This work provides a comprehensive review of existing vehicle detection algorithms and discusses their practical applications in the field of autonomous driving. First, we provide a brief description of the tasks, evaluation metrics, and datasets for vehicle detection. Second, more than 200 classical and latest vehicle detection algorithms are summarized in detail, including those based on machine vision, LiDAR, millimeter-wave radar, and sensor fusion. Finally, this article discusses the strengths and limitations of different algorithms and sensors, and proposes future trends.

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Section: Feature Extractionmentioning

confidence: 99%

Vehicle Detection Algorithms for Autonomous Driving: A Review

Liang,

Ma,

Zhao

et al. 2024

Sensors

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show abstract

“…To implement effective counting, many traditional techniques have been proposed to extract a suitable area first and then count this area by handcrafted features such as HOG [3] and SIFT. However, these handcrafted features are susceptible to external conditions.…”

Section: Introductionmentioning

confidence: 99%

Dense Vehicle Counting Estimation via a Synergism Attention Network

Jin

Wang

et al. 2022

Electronics

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Along with rising traffic jams, accurate counting of vehicles in surveillance images is becoming increasingly difficult. Current counting methods based on density maps have achieved tremendous improvement due to the prosperity of convolution neural networks. However, as highly overlapping and sophisticated large-scale variation phenomena often appear within dense images, neither traditional CNN methods nor fixed-size self-attention transformer methods can implement exquisite counting. To relieve these issues, in this paper, we propose a novel vehicle counting approach, namely the synergism attention network (SAN), by unifying the benefits of transformers and convolutions to perform dense counting assignments effectively. Specifically, a pyramid framework is designed to adaptively utilize the multi-level features for better fitting in counting tasks. In addition, a synergism transformer (SyT) block is customized, where a dual-transformer structure is equipped to capture global attention and location-aware information. Finally, a Location Attention Cumulation (LAC) module is also presented to explore the more efficient and meaningful weighting regions. Extensive experiments demonstrate that our model is very competitive and reached new state-of-the-art performance on TRANCOS datasets.

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