A Real Time Object Detection Approach Applied to Reliable Pedestrian Detection

Ma, Guanglin; Park, Su-Birm; Ioffe, A.; Müller-Schneiders, Stefan; Kummert, Anton

doi:10.1109/ivs.2007.4290207

Cited by 14 publications

(6 citation statements)

References 19 publications

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“…They also discussed video processing improvements and integration into a DaimlerChrysler test vehicle. Ma et al created a real-time obstacle and pedestrian detection system for vehicles using a single monochrome camera [102]. They identified obstacles above the ground plane with a "virtual stereo system" via inverse perspective mapping and used digital image stabilization for accuracy.…”

Section: Pedestrian and Traffic Detectionmentioning

confidence: 99%

Synchronizing Object Detection: Applications, Advancements and Existing Challenges

Tanzib Hosain,

Zaman,

Abir

et al. 2024

IEEE Access

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From pivotal roles in autonomous vehicles, healthcare diagnostics, and surveillance systems to seamlessly integrating with augmented reality, object detection algorithms stand as the cornerstone in unraveling the complexities of the visual world. Tracing the trajectory from conventional region-based methods to the latest neural network architectures reveals a technological renaissance where algorithms metamorphose into digital artisans. However, this journey is not without hurdles, prompting researchers to grapple with real-time detection, robustness in varied environments, and interpretability amidst the intricacies of deep learning. The allure of addressing issues such as occlusions, scale variations, and finegrained categorization propels exploration into uncharted territories, beckoning the scholarly community to contribute to an ongoing saga of innovation and discovery. This research offers a comprehensive panorama, encapsulating the applications reshaping our digital reality, the advancements pushing the boundaries of perception, and the open issues extending an invitation to the next generation of visionaries to explore uncharted frontiers within object detection.

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Section: Pedestrian and Traffic Detectionmentioning

confidence: 99%

Synchronizing Object Detection: Applications, Advancements and Existing Challenges

Tanzib Hosain,

Zaman,

Abir

et al. 2024

IEEE Access

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“…The stereo IPM can keep the non-plane objects and remove the plane objects such as lane-markings, shadows by comparing the differences between the left and right remapped image, which will be illustrated in Figure 10. According to the stereo IPM method [21], two cameras should be used to acquire the sufficient information of overlapped regions. Since this paper focuses on using a single camera, we take advantage of time difference to simulate the effects of stereo cameras.…”

Section: The Temporal Fisheye Lens Inverse Perspective Mappingmentioning

confidence: 99%

Construction of Fisheye Lens Inverse Perspective Mapping Model and Its Applications of Obstacle Detection

Lin

Shen

Shou

2010

EURASIP J. Adv. Signal Process.

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In this paper, we develop a vision based obstacle detection system by utilizing our proposed fisheye lens inverse perspective mapping (FLIPM) method. The new mapping equations are derived to transform the images captured by the fisheye lens camera into the undistorted remapped ones under practical circumstances. In the obstacle detection, we make use of the features of vertical edges on objects from remapped images to indicate the relative positions of obstacles. The static information of remapped images in the current frame is referred to determining the features of source images in the searching stage from either the profile or temporal IPM difference image. The profile image can be acquired by several processes such as sharpening, edge detection, morphological operation, and modified thinning algorithms on the remapped image. The temporal IPM difference image can be obtained by a spatial shift on the remapped image in the previous frame. Moreover, the polar histogram and its post-processing procedures will be used to indicate the position and length of feature vectors and to remove noises as well. Our obstacle detection can give drivers the warning signals within a limited distance from nearby vehicles while the detected obstacles are even with the quasi-vertical edges.

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“…First, the potential pedestrian candidates are detected and afterward, the detected candidates are fed into the classifier to do final verification. We also use 2 step detection strategy to do our pedestrian detection [2], [3]. Left image in figure 4 shows the detected pedestrian candidates (green bounding boxes), and right image in figure 4 shows the final pedestrian which is classified by our classifier.…”

Section: Figure 3 Pedestrian Detection Systemmentioning

confidence: 99%

“…DETECTION -The detection of pedestrian candidates is done by utilizing the Inverse perspective mapping (IPM) algorithm. Figure 6 shows the idea of inverse perspective mapping based obstacle detection algorithm [2], [ 3].…”

Section: Previous Workmentioning

confidence: 99%

A Symmetry Search and Filtering Algorithm for Vision Based Pedestrian Detection System

Kummert

Park

et al. 2008

SAE Technical Paper Series

Self Cite

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In this paper we present a fast symmetry search and filtering algorithm for monocular vision based pedestrian candidate detection application. First the ROI of symmetry search is focused on the pedestrian leg region, where the background is relatively simple ground plane. Afterward, the search region is divided into 2 x 4 sub blocks and symmetry density and distribution of each sub block is calculated. Finally, by comparing the symmetry density and distribution of the sub blocks, the correct symmetry axis of the pedestrian candidate is search and also some no-pedestrian candidates are filtered out. The results shown in this method are fast, cost effective and well suited for real-time vision applications.

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A Real Time Object Detection Approach Applied to Reliable Pedestrian Detection

Cited by 14 publications

References 19 publications

Synchronizing Object Detection: Applications, Advancements and Existing Challenges

Synchronizing Object Detection: Applications, Advancements and Existing Challenges

Construction of Fisheye Lens Inverse Perspective Mapping Model and Its Applications of Obstacle Detection

A Symmetry Search and Filtering Algorithm for Vision Based Pedestrian Detection System

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