Road Object Detection Using a Disparity-Based Fusion Model

Chen, Jing; Xu, Wenqiang; Peng, Weimin; Bu, Wanghui; Xing, Baixi; Luo, Geng

doi:10.1109/access.2018.2825229

Cited by 8 publications

(4 citation statements)

References 31 publications

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“…The first is based on vehicle appearance, and the second is based on vehicle motion. The appearancebased strategies depend mainly on visual features, including vehicle symmetry, texture, edges, and color [29]- [32]. The concept of motion-based techniques is to extract the moving vehicles based on their motion characteristics that separate them from the background, such as optical flow, frame differences, and background subtraction [33]- [35].…”

Section: Vehicle Detectionmentioning

confidence: 99%

The Traffic Scene Understanding and Prediction Based on Image Captioning

Song

et al. 2021

IEEE Access

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Section: Vehicle Detectionmentioning

confidence: 99%

The Traffic Scene Understanding and Prediction Based on Image Captioning

Song

et al. 2021

IEEE Access

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“…The vehicle detection algorithms based on machine vision mainly include the method based on motion information detection [8][9][10], the method based on prior knowledge detection [11][12][13], the detection method based on stereo information [14][15][16] and the detection method based on machine learning [17][18][19]. Compared with other detection methods based on machine vision, detection methods based on machine learning are more outstanding in recognition performance and robustness [20].…”

Section: Introductionmentioning

confidence: 99%

Moving-Vehicle Identification Based on Hierarchical Detection Algorithm

et al. 2021

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The vehicle detection method plays an important role in the driver assistance system. Therefore, it is very important to improve the real-time performance of the detection algorithm. Nowadays, the most popular method is the scanning method based on sliding window search, which detects the vehicle from the image to be detected. However, the existing sliding window detection algorithm has many drawbacks, such as large calculation amount and poor real-time performance, and it is impossible to detect the target vehicle in real time during the motion process. Therefore, this paper proposes an improved hierarchical sliding window detection algorithm to detect moving vehicles in real time. By extracting the region of interest, the region of interest is layered, the maximum and minimum values of the detection window in each layer are set, the flashing frame generated by the layering is eliminated by the delay processing method, and a method suitable for the motion is obtained: the real-time detection algorithm of the vehicle, that is, the hierarchical sliding window detection algorithm. The experiments show that the more layers are divided, the more time is needed, and when the number of detection layers is greater than 7, the time change rate increases significantly. As the number of layers decreases, the detection accuracy rate also decreases, resulting in the phenomenon of a false positive. Therefore, it is determined to meet the requirements of real time and accuracy when the image is divided into 7 layers. It can be seen from the experiment that when the images to be detected are divided into 7 layers and the maximum and minimum values of detection windows are 30 × 30 and 250 × 250, respectively, the number of sub-windows generated is one thirty-seventh of the original sliding window detection algorithm, and the execution time is only one-third of the original sliding window detection algorithm. This shows that the hierarchical sliding window detection algorithm has better real-time performance than the original sliding window detection algorithm.

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“…The most recent studies in intelligent transportation systems focus on vehicle detection [3,4,5,6,7,8,9,10,11,12]. Vehicle detection can be categorized into two groups [1]: detection methods based on vehicle appearance, and detection methods based on vehicle motion.…”

Section: Introductionmentioning

confidence: 99%

“…Vehicle detection can be categorized into two groups [1]: detection methods based on vehicle appearance, and detection methods based on vehicle motion. The appearance-based strategies depend mainly on visual features, including vehicle symmetry, texture, edges, and color [5,6,7,8]. The concept of motion-based techniques is to extract the moving vehicles based on their motion characteristic that separate them from the background, such as optical flow, frame differences, and background subtraction [9,10,11].…”

Section: Introductionmentioning

confidence: 99%

Robust Vehicle Detection and Counting Algorithm Employing a Convolution Neural Network and Optical Flow

Gomaa

Abdelwahab

Abo‐Zahhad

et al. 2019

Sensors

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Automatic vehicle detection and counting are considered vital in improving traffic control and management. This work presents an effective algorithm for vehicle detection and counting in complex traffic scenes by combining both convolution neural network (CNN) and the optical flow feature tracking-based methods. In this algorithm, both the detection and tracking procedures have been linked together to get robust feature points that are updated regularly every fixed number of frames. The proposed algorithm detects moving vehicles based on a background subtraction method using CNN. Then, the vehicle’s robust features are refined and clustered by motion feature points analysis using a combined technique between KLT tracker and K-means clustering. Finally, an efficient strategy is presented using the detected and tracked points information to assign each vehicle label with its corresponding one in the vehicle’s trajectories and truly counted it. The proposed method is evaluated on videos representing challenging environments, and the experimental results showed an average detection and counting precision of 96.3% and 96.8%, respectively, which outperforms other existing approaches.

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Road Object Detection Using a Disparity-Based Fusion Model

Cited by 8 publications

References 31 publications

The Traffic Scene Understanding and Prediction Based on Image Captioning

The Traffic Scene Understanding and Prediction Based on Image Captioning

Moving-Vehicle Identification Based on Hierarchical Detection Algorithm

Robust Vehicle Detection and Counting Algorithm Employing a Convolution Neural Network and Optical Flow

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