Design of a Robust System Architecture for Tracking Vehicle on Highway Based on Monocular Camera

Wu, Zhihong; Li, Fuxiang; Zhu, Yuan; Ke, Lei; Wu, Mingzhi

doi:10.3390/s22093359

Cited by 6 publications

(4 citation statements)

References 21 publications

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“…Before using machine vision measurement technology for high-precision measurement, camera calibration is first performed to obtain the internal and external parameters of the camera [34]. The camera involves the world coordinate system (X w , Y w , Z w ) , camera coordinate system (X s , Y s , Z s ) , imaging coordinate system (x, y) , and pixel coordinate system (u 0 , v 0 ) in the process of imaging transformation.…”

Section: Monocular Camera Coordinate Transformationmentioning

confidence: 99%

Cognitive Enhancement of Robot Path Planning and Environmental Perception Based on Gmapping Algorithm Optimization

Liu,

Gong,

et al. 2024

Electronics

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In the logistics warehouse environment, the autonomous navigation and environment perception of the logistics sorting robot are two key challenges. To deal with the complex obstacles and cargo layout in a warehouse, this study focuses on improving the robot perception and navigation system to achieve efficient path planning and safe motion control. For this purpose, a scheme based on an improved Gmapping algorithm is proposed to construct a high-precision map inside a warehouse through the efficient scanning and processing of environmental data by robots. While the improved algorithm effectively integrates sensor data with robot position information to realize the real-time modeling and analysis of warehouse environments. Consequently, the precise mapping results provide a reliable navigation basis for the robot, enabling it to make intelligent path planning and obstacle avoidance decisions in unknown or dynamic environments. The experimental results show that the robot using the improved Gmapping algorithm has high accuracy and robustness in identifying obstacles and an effectively reduced navigation error, thus improving the intelligence level and efficiency of logistics operations. The improved algorithm significantly enhances obstacle detection rates, increasing them by 4.05%. Simultaneously, it successfully reduces map size accuracy errors by 1.4% and angle accuracy errors by 0.5%. Additionally, the accuracy of the robot’s travel distance improves by 2.4%, and the mapping time is reduced by nine seconds. Significant progress has been made in achieving high-precision environmental perception and intelligent navigation, providing reliable technical support and solutions for autonomous operations in logistics warehouses.

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Section: Monocular Camera Coordinate Transformationmentioning

confidence: 99%

Cognitive Enhancement of Robot Path Planning and Environmental Perception Based on Gmapping Algorithm Optimization

Liu,

Gong,

et al. 2024

Electronics

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“…In order to show the effectiveness of the proposed risk minimization strategy, we conduct a case study on a YOLO (You Only Look Once) based AEBS with OOD detector. This AEBS uses monocular vision to identify obstacles as demonstrated in [27]. The output of the YOLO object detector is processed to make a binary decision: 0 (do nothing) or 1 (engage emergency braking).…”

Section: Case Study: Yolo Based Aebsmentioning

confidence: 99%

“…As proposed in [27], we use YOLO to perform object detection. Specifically, we select the YOLOv7 tiny model [22], as this provides state of the art object detection performance while keeping execution time low with respect to other YOLO variants.…”

Section: B Object Detectormentioning

confidence: 99%

Design Methodology for Deep Out-of-Distribution Detectors in Real-Time Cyber-Physical Systems

Yuhas

Easwaran

2022

2022 IEEE 28th International Conference on Embedded and Real-Time Computing Systems and Applications (RTCSA)

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Learning enabled components (LECs), while critical for decision making in autonomous vehicles (AVs), are likely to make incorrect decisions when presented with samples outside of their training distributions. Out-of-distribution (OOD) detectors have been proposed to detect such samples, thereby acting as a safety monitor, however, both OOD detectors and LECs require heavy utilization of embedded hardware typically found in AVs. For both components, there is a tradeoff between non-functional and functional performance, and both impact a vehicle's safety. For instance, giving an OOD detector a longer response time can increase its accuracy at the expense of the LEC. We consider an LEC with binary output like an autonomous emergency braking system (AEBS) and use risk, the combination of severity and occurrence of a failure, to model the effect of both components' design parameters on each other's functional and non-functional performance, as well as their impact on system safety. We formulate a codesign methodology that uses this risk model to find the design parameters for an OOD detector and LEC that decrease risk below that of the baseline system and demonstrate it on a vision based AEBS. Using our methodology, we achieve a 42.3% risk reduction while maintaining equivalent resource utilization.

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“…Object tracking is constantly determining a moving objectʹs trajectory from measurements taken by one or more sensors [1]. Single-object tracking (SOT) [2] and Multi-object tracking (MOT) [3][4][5][6][7] are two main categories of object tracking methods (MOT). When using SOT, the tracker follows a single, predetermined object.…”

Section: Introductionmentioning

confidence: 99%

Improved DeepSORT-Based Object Tracking in Foggy Weather for AVs Using Sematic Labels and Fused Appearance Feature Network

Ogunrinde¹,

Bernadin²

2023

Preprint

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The presence of fog in the background can prevent small and distant objects from being detected, let alone tracked. Under safety-critical conditions, multi-object tracking models require faster-tracking speed while maintaining high object-tracking accuracy. The original DeepSORT algorithm used YOLOv4 for the detection phase, and a simple neural network for deep appearance descriptor. Consequently, the feature map generated loses relevant details about the track being matched with a given detection in fog. Targets with a high degree of appearance similarity on the detection frame are more likely to be mismatched, resulting in identity switches or track failures in heavy fog. We propose an improved multi-object tracking model based on the DeepSORT algorithm to im-prove tracking accuracy and speed under foggy weather conditions. First, we employed our camera-radar fusion network (CR-YOLOnet) in the detection phase for faster and more accurate object detection. We proposed an appearance feature network to replace the basic convolutional neural network. We incorporated GhostNet to take the place of the traditional convolutional layers to generate more features and reduce computational complexities and cost. We adopted a segmentation module and fed the semantic labels of the corresponding input frame to add rich semantic information to the low-level appearance feature maps. Our proposed method outperformed YOLOv5 + DeepSORT with a 35.15% increase in multi-object tracking accuracy, a 32.65% increase in multi-object tracking precision, the speed increased by 37.56%, and identity switches decreased by 46.81%.

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Design of a Robust System Architecture for Tracking Vehicle on Highway Based on Monocular Camera

Cited by 6 publications

References 21 publications

Cognitive Enhancement of Robot Path Planning and Environmental Perception Based on Gmapping Algorithm Optimization

Cognitive Enhancement of Robot Path Planning and Environmental Perception Based on Gmapping Algorithm Optimization

Design Methodology for Deep Out-of-Distribution Detectors in Real-Time Cyber-Physical Systems

Improved DeepSORT-Based Object Tracking in Foggy Weather for AVs Using Sematic Labels and Fused Appearance Feature Network

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