Vision-based counting of pedestrians and cyclists

Kocamaz, Mehmet Kemal; Gong, Jianhua; Pires, Bernardo R.

doi:10.1109/wacv.2016.7477685

Cited by 18 publications

(11 citation statements)

References 22 publications

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“…Besides, Zhao [18] and Kumar [19] present combining of 3D LiDAR and camera data. Kocamaz et al proposed a map supervised scheme for road detection [20,21].…”

Section: Journal Of Sensorsmentioning

confidence: 99%

Real‐Time Object Detection for LiDAR Based on LS‐R‐YOLOv4 Neural Network

et al. 2021

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Recently, self-driving cars became a big challenge in the automobile industry. After the DARPA challenge, which introduced the design of a self-driving system that can be classified as SAR Level 3 or higher levels, driven to focus on self-driving cars more. Later on, using these introduced design models, a lot of companies started to design self-driving cars. Various sensors, such as radar, high-resolution cameras, and LiDAR are important in self-driving cars to sense the surroundings. LiDAR acts as an eye of a self-driving vehicle, by offering 64 scanning channels, 26.9° vertical field view, and a high-precision 360° horizontal field view in real-time. The LiDAR sensor can provide 360° environmental depth information with a detection range of up to 120 meters. In addition, the left and right cameras can further assist in obtaining front image information. In this way, the surrounding environment model of the self-driving car can be accurately obtained, which is convenient for the self-driving algorithm to perform route planning. It is very important for self-driving to avoid the collision. LiDAR provides both horizontal and vertical field views and helps in avoiding collision. In an online website, the dataset provides different kinds of data like point cloud data and color images which helps this data to use for object recognition. In this paper, we used two types of publicly available datasets, namely, KITTI and PASCAL VOC. Firstly, the KITTI dataset provides in-depth data knowledge for the LiDAR segmentation (LS) of objects obtained through LiDAR point clouds. The performance of object segmentation through LiDAR cloud points is used to find the region of interest (ROI) on images. And later on, we trained the network with the PASCAL VOC dataset used for object detection by the YOLOv4 neural network. To evaluate, we used the region of interest image as input to YOLOv4. By using all these technologies, we can segment and detect objects. Our algorithm ultimately constructs a LiDAR point cloud at the same time; it also detects the image in real-time.

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“…Besides, Zhao [18] and Kumar [19] present combining of 3D LiDAR and camera data. Kocamaz et al proposed a map supervised scheme for road detection [20,21].…”

Section: Journal Of Sensorsmentioning

confidence: 99%

Real‐Time Object Detection for LiDAR Based on LS‐R‐YOLOv4 Neural Network

et al. 2021

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“…Similar sectors, such as roadways and airports, have begun to implement these techniques for video big data analysis. Selected AI techniques include background subtraction, region of interest, and Kalman filtering (13)(14)(15)(16). The first and most fundamental tool in video analytics is background subtraction.…”

Section: Ai Technologies For Video Analyticsmentioning

confidence: 99%

“…A user can define a line or polygon of pixels in the frame which an AI can use as a reference. In that study, pedestrians and cyclists were tracked in the frame and only counted as “crossing” if they passed through the ROI ( 16 ). Another AI technique is the Kalman filter, which is a set of mathematical equations to estimate the state of a process ( 14 ).…”

Section: Literature Reviewmentioning

confidence: 99%

Video Analytics for Railroad Safety Research: An Artificial Intelligence Approach

Zaman

Liu

Zhang

2018

Transportation Research Record

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The volume of video data in the railroad industry has increased significantly in recent years. Surveillance cameras are situated on nearly every part of the railroad system, such as inside the cab, along the track, at grade crossings, and in stations. These camera systems are manually monitored, either live or subsequently reviewed in an archive, which requires an immense amount of human resources. To make the video analysis much less labor-intensive, this paper develops a framework for utilizing artificial intelligence (AI) technologies for the extraction of useful information from these big video datasets. This framework has been implemented based on the video data from one grade crossing in New Jersey. The AI algorithm can automatically detect unsafe trespassing of railroad tracks (called near-miss events in this paper). To date, the AI algorithm has analyzed hours of video data and correctly detected all near-misses. This pilot study indicates the promise of using AI for automated analysis of railroad video big data, thereby supporting data-driven railroad safety research. For practical use, our AI algorithm has been packaged into a computer-aided decision support tool (named AI-Grade) that outputs near-miss video clips based on user-provided raw video data. This paper and its sequent studies aim to provide the railroad industry with next-generation big data analysis methods and tools for quickly and reliably processing large volumes of video data in order to better understand human factors in railroad safety research.

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“…This research allows to annotate automatically using OpenStreetMap [2]. Kocamaz et al [3] suggest the vision-based pedestrian and cyclist detection method with the multicue cluster algorithm designed to reduce false alarms. Another important benchmark in the autonomous car is to efficiently control in the real road.…”

Section: Introductionmentioning

confidence: 99%

Reinforcement Learning Guided by Double Replay Memory

Han

Lim

et al. 2021

Journal of Sensors

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Experience replay memory in reinforcement learning enables agents to remember and reuse past experiences. Most of the reinforcement models are subject to single experience replay memory to operate agents. In this article, we propose a framework that accommodates doubly used experience replay memory, exploiting both important transitions and new transitions simultaneously. In numerical studies, the deep Q -networks (DQN) equipped with double experience replay memory are examined under various scenarios. A self-driving car requires an automated agent to figure out when to adequately change lanes on the real-time basis. To this end, we apply our proposed agent to the simulation of urban mobility (SUMO) experiments. Besides, we also verify its applicability to reinforcement learning whose action space is discrete (e.g., computer game environments). Taken all together, we conclude that the proposed framework outperforms priorly known reinforcement learning models in the virtue of double experience replay memory.

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Vision-based counting of pedestrians and cyclists

Cited by 18 publications

References 22 publications

Real‐Time Object Detection for LiDAR Based on LS‐R‐YOLOv4 Neural Network

Real‐Time Object Detection for LiDAR Based on LS‐R‐YOLOv4 Neural Network

Video Analytics for Railroad Safety Research: An Artificial Intelligence Approach

Reinforcement Learning Guided by Double Replay Memory

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