AIMC Topic: Pedestrians

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Enhancing Detection Quality Rate with a Combined HOG and CNN for Real-Time Multiple Object Tracking across Non-Overlapping Multiple Cameras.

Sensors (Basel, Switzerland)
Multi-object tracking in video surveillance is subjected to illumination variation, blurring, motion, and similarity variations during the identification process in real-world practice. The previously proposed applications have difficulties in learni...

Contextual Detection of Pedestrians and Vehicles in Orthophotography by Fusion of Deep Learning Algorithms.

Sensors (Basel, Switzerland)
In the context of smart cities, monitoring pedestrian and vehicle movements is essential to recognize abnormal events and prevent accidents. The proposed method in this work focuses on analyzing video streams captured from a vertically installed came...

Multi-Task Learning With Coarse Priors for Robust Part-Aware Person Re-Identification.

IEEE transactions on pattern analysis and machine intelligence
Part-level representations are important for robust person re-identification (ReID), but in practice feature quality suffers due to the body part misalignment problem. In this paper, we present a robust, compact, and easy-to-use method called the Mul...

A Review of Deep Learning-Based Methods for Pedestrian Trajectory Prediction.

Sensors (Basel, Switzerland)
Pedestrian trajectory prediction is one of the main concerns of computer vision problems in the automotive industry, especially in the field of advanced driver assistance systems. The ability to anticipate the next movements of pedestrians on the str...

Social Robot Navigation Tasks: Combining Machine Learning Techniques and Social Force Model.

Sensors (Basel, Switzerland)
Social robot navigation in public spaces, buildings or private houses is a difficult problem that is not well solved due to environmental constraints (buildings, static objects etc.), pedestrians and other mobile vehicles. Moreover, robots have to mo...

Effect of pedestrian physique differences on head injury prediction in car-to-pedestrian accidents using deep learning.

Traffic injury prevention
OBJECTIVE: The aim of this study is to identify the effects of pedestrian physique differences on head injury prediction in car-to-pedestrian accidents via deep learning.

Investigating yielding behavior of heterogeneous vehicles at a semi-controlled crosswalk.

Accident; analysis and prevention
It is well known that pedestrians are vulnerable road users. Their risk of being injured or killed in road traffic crashes is even higher as vehicle drivers often violate traffic rules and do not slow down or yield in front of crosswalks. In order to...

Crash severity analysis of vulnerable road users using machine learning.

PloS one
Road crash fatality is a universal problem of the transportation system. A massive death toll caused annually due to road crash incidents, and among them, vulnerable road users (VRU) are endangered with high crash severity. This paper focuses on empl...

A Review of Intelligent Driving Pedestrian Detection Based on Deep Learning.

Computational intelligence and neuroscience
Pedestrian detection is a specific application of object detection. Compared with general object detection, it shows similarities and unique characteristics. In addition, it has important application value in the fields of intelligent driving and sec...

A Two-Stream Dynamic Pyramid Representation Model for Video-Based Person Re-Identification.

IEEE transactions on image processing : a publication of the IEEE Signal Processing Society
Video-based person re-identification (Re-ID) leverages rich spatio-temporal information embedded in sequence data to further improve the retrieval accuracy comparing with single image Re-ID. However, it also brings new difficulties. 1) Both spatial a...