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Cut-in vehicle warning system exploiting multiple rotational images of SVM cameras

EXPERT SYSTEMS WITH APPLICATIONS 2019年 125卷 81-99页

作者： Choi, Kyoungtaek Jung, Ho Gi Sejong Univ Sch Intelligent Mech Engn 209 Neungdong Ro Seoul 05006 South Korea Korea Natl Univ Transportat Dept Elect Engn 50 Daehak Ro Daesowon Myeon Chungju Si 27469 Chungcheongbuk South Korea

Smart cruise control (SCC) is one of the representative systems of advanced driver assistance systems (ADAS). Conventional SCC systems have a problem in that they cannot detect a cut-in vehicle in a short distance because the field of view (FOV) of their sensors for detecting vehicles ahead is limited. To solve this problem, this paper proposes a novel method using surround view monitoring (SVM) cameras which can observe the surroundings of a host vehicle without blind spots. To reduce the variation of projected appearance according to the relative position, this paper proposes to exploit multiple virtual camera images (rotational images). In each rotational image, vehicles are detected using a part-based method: tires are detected and then vehicles are detected by combining the tires. Detected tires in multiple rotational images are integrated and tracked in 3D space. Finally, the proposed method determines whether a vehicle has intruded into the cut-in zone based on the position and orientation of the vehicle. The proposed method succeeds to detect vehicle even by a simple method thanks to the rotational image even without any special HAW. The proposed method uses only the sensor adopted in mass-produced vehicles and it is very practical. Moreover, the method can estimate the surrounding vehicle position accurately enough to be used in the braking control system. The performance of the proposed method is evaluated with various driving situations, of which the total length is 447.8 min (43.5 min for the test track and 404.3 min for natural driving). Among 211 cut-in events, 208 events are correctly warned and only 3 false warnings occur. Although the proposed method simultaneously processes the rotational images generated from three SVM cameras (forward, left, and right), the average processing time per frame takes about 58 ms. (C) 2019 Elsevier Ltd. All rights reserved.

关键词： part-based object detection Vehicle detection Surround view monitoring Cut-in warning Rotational images

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AN EVALUATION OF DEEP LEARNING TECHNIQUES FOR QR CODE detection 26

AN EVALUATION OF DEEP LEARNING TECHNIQUES FOR QR CODE DETECT...

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26th IEEE International Conference on Image Processing (ICIP)

作者： Blanger, Leonardo Hirata, Nina S. T. Univ Sao Paulo Inst Math & Stat Rua Matao 1010 Sao Paulo Brazil

ISBN: (纸本)9781538662496

In this work, we employ deep learning models for detecting QR Codes in natural scenes. A series of different model configurations are evaluated in terms of Average Precision, and an architecture modification that allows detection aided by object subparts annotations is proposed. This modification is implemented in our best scoring model, which is compared to a traditional technique, achieving a substantial improvement in the considered metrics. The dataset used in our evaluation, with bounding box annotations for both QR Codes and their Finder Patterns (FIPs), will be made publicly available. This dataset is significantly bigger than known available options at the moment, so we expect it to provide a common benchmark tool for QR Code detection in natural scenes.

关键词： QR code deep learning single shot detector part-based object detection

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Vehicle detection grammars with partial occlusion handling for traffic surveillance

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TRANSPORTATION RESEARCH part C-EMERGING TECHNOLOGIES 2015年 56卷 80-93页

作者： Tian, Bin Tang, Ming Wang, Fei-Yue Chinese Acad Sci Inst Automat State Key Lab Management & Control Complex Syst Beijing 100190 Peoples R China Chinese Acad Sci Inst Automat Natl Lab Pattern Recognit Beijing 100190 Peoples R China

Traffic surveillance is an important topic in intelligent transportation systems (ITS). Robust vehicle detection is one challenging problem for complex traffic surveillance. In this paper, we propose an efficient vehicle detection method by designing vehicle detection grammars and handling partial occlusion. The grammar model is implemented by novel detection grammars, including structure, deformation and pairwise SVM grammars. First, the vehicle is divided into its constitute parts, called semantic parts, which can represent the vehicle effectively. To increase the robustness of part detection, the semantic parts are represented by their detection score maps. The semantic parts are further divided into sub-parts automatically. The two-layer division of the vehicle is modeled into a grammar model. Then, the grammar model is trained by a designed training procedure to get ideal grammar parameters, including appearance models and grammar productions. After that, vehicle detection is executed by a designed detection procedure with respect to the grammar model. Finally, the issue of vehicle occlusion is handled by designing and training specific grammars. The strategy adopted by our method is first to divide the vehicle into the semantic parts and sub-parts, then to train the grammar productions for semantic parts and sub-parts by introducing novel pairwise SVM grammars and finally to detect the vehicle by applying the trained grammars. Experiments in practical urban scenarios are carried out for complex traffic surveillance. It can be shown that our method adapts to partial occlusion and various challenging cases. (C) 2015 Elsevier Ltd. All rights reserved.

关键词： Computer vision Grammar model Occlusion handling part-based object detection Vehicle detection

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Rear-View Vehicle detection and Tracking by Combining Multiple parts for Complex Urban Surveillance

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IEEE TRANSACTIONS ON INTELLIGENT TRANSPORTATION SYSTEMS 2014年第2期15卷 597-606页

作者： Tian, Bin Li, Ye Li, Bo Wen, Ding Chinese Acad Sci Inst Automat State Key Lab Management & Control Complex Syst Beijing 100190 Peoples R China Natl Univ Def Technol Ctr Mil Computat Expt & Parallel Syst Changsha 410073 Hunan Peoples R China

Traffic surveillance is an important topic in intelligent transportation systems. Robust vehicle detection and tracking is one challenging problem for complex urban traffic surveillance. This paper proposes a rear-view vehicle detection and tracking method based on multiple vehicle salient parts using a stationary camera. We show that spatial modeling of these vehicle parts is crucial for overall performance. First, the vehicle is treated as an object composed of multiple salient parts, including the license plate and rear lamps. These parts are localized using their distinctive color, texture, and region feature. Furthermore, the detected parts are treated as graph nodes to construct a probabilistic graph using a Markov random field model. After that, the marginal posterior of each part is inferred using loopy belief propagation to get final vehicle detection. Finally, the vehicles' trajectories are estimated using a Kalman filter, and a tracking-based detection technique is realized. Experiments in practical urban scenarios are carried out under various weather conditions. It can be shown that our method adapts to partial occlusion and various lighting conditions. Experiments also show that our method can achieve real-time performance.

关键词： Kalman filter (KF) Markov random field (MRF) part-based object detection tracking vehicle detection

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part-based Pedestrian detection using Grammar Model and ABM-HoG Features

Part-based Pedestrian Detection using Grammar Model and ABM-...

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IEEE International Conference on Vehicular Electronics and Safety (ICVES)

作者： Li, Bo Li, Ye Tian, Bin Zhu, Fenghua Xiong, Gang Wang, Kunfeng Chinese Acad Sci Inst Automat State Key Lab Management & Control Complex Syst Beijing Peoples R China

ISBN: (纸本)9781479903801

To handle the pedestrian appearance and pose variations in complex traffic environments, we present one part-based pedestrian detection approach using a stochastic grammar model in this paper. The And-Or graph model is introduced to represent the human body as an assembly of compositional and reconfigurable parts. Thus, the task of detection is converted into the human parsing problem, which is a Bayesian inference process. We model the appearance of pedestrian parts in a rich feature representation. This appearance model enhances the Histogram of Gradients (HoG) map with Active Basis Model (ABM), which is a sparse deformable template depicting salient structures of objects. Then, geometry constraints among parts are described by Gaussian distributions. Finally, the bottom-up parsing inference is conducted by aggregating scores to get the pedestrian detection responses. In experiments, we show the superiority of our appearance model, as well as the reliable pedestrian detection results of our approach in complex traffic scenes.

关键词： pedestrian detection grammar model part-based object detection

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