Pedestrian Detection and Tracking from Low-Resolution Unmanned Aerial Vehicle Thermal Imagery

Ma, Yalong; Wu, Xinkai; Yu, Guizhen; Xu, Yongzheng; Wang, Yunpeng

doi:10.3390/s16040446

Cited by 81 publications

(56 citation statements)

References 35 publications

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“…4 As illustrated in Table 6 and throughout this section, most research focuses on short-term path prediction based on visible cameras [185,189,203]. Although there has been studies undertaken for using thermal sensors to tracking pedestrians/cyclists (see [204,205]), there has not been as much focus on using thermal data for pedestrian/cyclist intent estimation. Therefore it may be useful to further investigate the improvements that may be brought from sensor fusion for VRU intent estimation in the same way as sensor fusion for VRU detection was discussed in Section 7.…”

Section: Cnn and Svmmentioning

confidence: 99%

Pedestrian and Cyclist Detection and Intent Estimation for Autonomous Vehicles: A Survey

et al. 2019

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As autonomous vehicles become more common on the roads, their advancement draws on safety concerns for vulnerable road users, such as pedestrians and cyclists. This paper presents a review of recent developments in pedestrian and cyclist detection and intent estimation to increase the safety of autonomous vehicles, for both the driver and other road users. Understanding the intentions of the pedestrian/cyclist enables the self-driving vehicle to take actions to avoid incidents. To make this possible, development of methods/techniques, such as deep learning (DL), for the autonomous vehicle will be explored. For example, the development of pedestrian detection has been significantly advanced using DL approaches, such as; Fast Region-Convolutional Neural Network (R-CNN) , Faster R-CNN and Single Shot Detector (SSD). Although DL has been around for several decades, the hardware to realise the techniques have only recently become viable. Using these DL methods for pedestrian and cyclist detection and applying it for the tracking, motion modelling and pose estimation can allow for a successful and accurate method of intent estimation for the vulnerable road users. Although there has been a growth in research surrounding the study of pedestrian detection using vision-based approaches, further attention should include focus on cyclist detection. To further improve safety for these vulnerable road users (VRUs), approaches such as sensor fusion and intent estimation should be investigated.

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Section: Cnn and Svmmentioning

confidence: 99%

Pedestrian and Cyclist Detection and Intent Estimation for Autonomous Vehicles: A Survey

et al. 2019

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“…As ViBe [6] and frame difference [7] are sensitive to background motions, image registration [28] is applied first to compensate UAV motions and delete UAV video jitters. The time for image registration is included in the detection time for these two methods.…”

Section: Description Of Algorithms For Comparisonmentioning

confidence: 99%

Car Detection from Low-Altitude UAV Imagery with the Faster R-CNN

Wang

et al. 2017

Journal of Advanced Transportation

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127

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UAV based traffic monitoring holds distinct advantages over traditional traffic sensors, such as loop detectors, as UAVs have higher mobility, wider field of view, and less impact on the observed traffic. For traffic monitoring from UAV images, the essential but challenging task is vehicle detection. This paper extends the framework of Faster R-CNN for car detection from low-altitude UAV imagery captured over signalized intersections. Experimental results show that Faster R-CNN can achieve promising car detection results compared with other methods. Our tests further demonstrate that Faster R-CNN is robust to illumination changes and cars' in-plane rotation. Besides, the detection speed of Faster R-CNN is insensitive to the detection load, that is, the number of detected cars in a frame; therefore, the detection speed is almost constant for each frame. In addition, our tests show that Faster R-CNN holds great potential for parking lot car detection. This paper tries to guide the readers to choose the best vehicle detection framework according to their applications. Future research will be focusing on expanding the current framework to detect other transportation modes such as buses, trucks, motorcycles, and bicycles.

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“…Such technical issues may impose limitations to transportation professionals in a variety of intensive research and applies uses. Recently, by using UAV images, Xu et al [16] developed a new hybrid vehicle detection scheme which integrated the Viola-Jones and linear Support [17] developed a pedestrian detection and tracking system using thermal infrared images recorded from UAVs. The proposed detection and tracking approaches would facilitate a more detailed analysis of road users' behavior and interaction based on accurate trajectory data extracted from UAV video.…”

Section: Uav Applications In Transportation Engineering Andmentioning

confidence: 99%

“…In order to investigate pedestrian-vehicle conflict, road users should be detected and then tracked frame-to-frame in UAV video. In this study, we extract the trajectories for vehicles and pedestrians, respectively, at intervals of every 0.04 s by using the detection and tracking system developed in our previous studies [16,17]. A brief introduction is provided below.…”

Section: Detection and Trackingmentioning

confidence: 99%

Surrogate Safety Analysis of Pedestrian-Vehicle Conflict at Intersections Using Unmanned Aerial Vehicle Videos

Chen

Zeng

et al. 2017

Journal of Advanced Transportation

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Conflict analysis using surrogate safety measures (SSMs) has become an efficient approach to investigate safety issues. The state-of-the-art studies largely resort to video images taken from high buildings. However, it suffers from heavy labor work, high cost of maintenance, and even security restrictions. Data collection and processing remains a common challenge to traffic conflict analysis. Unmanned Aerial Systems (UASs) or Unmanned Aerial Vehicles (UAVs), known for easy maneuvering, outstanding flexibility, and low costs, are considered to be a novel aerial sensor. By taking full advantage of the bird’s eye view offered by UAV, this study, as a pioneer work, applied UAV videos for surrogate safety analysis of pedestrian-vehicle conflicts at one urban intersection in Beijing, China. Aerial video sequences for a period of one hour were analyzed. The detection and tracking systems for vehicle and pedestrian trajectory data extraction were developed, respectively. Two SSMs, that is, Postencroachment Time (PET) and Relative Time to Collision (RTTC), were employed to represent how spatially and temporally close the pedestrian-vehicle conflict is to a collision. The results of analysis showed a high exposure of pedestrians to traffic conflict both inside and outside the crosswalk and relatively risking behavior of right-turn vehicles around the corner. The findings demonstrate that UAV can support intersection safety analysis in an accurate and cost-effective way.

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Pedestrian Detection and Tracking from Low-Resolution Unmanned Aerial Vehicle Thermal Imagery

Cited by 81 publications

References 35 publications

Pedestrian and Cyclist Detection and Intent Estimation for Autonomous Vehicles: A Survey

Pedestrian and Cyclist Detection and Intent Estimation for Autonomous Vehicles: A Survey

Car Detection from Low-Altitude UAV Imagery with the Faster R-CNN

Surrogate Safety Analysis of Pedestrian-Vehicle Conflict at Intersections Using Unmanned Aerial Vehicle Videos

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