AIMC Topic: Pedestrians

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An End-to-End Foreground-Aware Network for Person Re-Identification.

IEEE transactions on image processing : a publication of the IEEE Signal Processing Society
Person re-identification is a crucial task of identifying pedestrians of interest across multiple surveillance camera views. For person re-identification, a pedestrian is usually represented with features extracted from a rectangular image region tha...

Prediction of pedestrian-vehicle conflicts at signalized intersections based on long short-term memory neural network.

Accident; analysis and prevention
Pedestrian protection is an important component of road safety. Intersections are dangerous locations for pedestrians with mixed traffic. This paper aims to predict potential traffic conflicts between pedestrians and vehicles at signalized intersecti...

Virtual to Real Adaptation of Pedestrian Detectors.

Sensors (Basel, Switzerland)
Pedestrian detection through Computer Vision is a building block for a multitude of applications. Recently, there has been an increasing interest in convolutional neural network-based architectures to execute such a task. One of these supervised netw...

Feature Channel Expansion and Background Suppression as the Enhancement for Infrared Pedestrian Detection.

Sensors (Basel, Switzerland)
Pedestrian detection is an important task in many intelligent systems, particularly driver assistance systems. Recent studies on pedestrian detection in infrared (IR) imagery have employed data-driven approaches. However, two problems in deep learnin...

Adopting Machine Learning and Spatial Analysis Techniques for Driver Risk Assessment: Insights from a Case Study.

International journal of environmental research and public health
Traffic violations usually caused by aggressive driving behavior are often seen as a primary contributor to traffic crashes. Violations are either caused by an unintentional or deliberate act of drivers that jeopardize the lives of fellow drivers, pe...

RNN-Aided Human Velocity Estimation from a Single IMU.

Sensors (Basel, Switzerland)
Pedestrian Dead Reckoning (PDR) uses inertial measurement units (IMUs) and combines velocity and orientation estimates to determine a position. The estimation of the velocity is still challenging, as the integration of noisy acceleration and angular ...

Hybrid SVM-CNN Classification Technique for Human-Vehicle Targets in an Automotive LFMCW Radar.

Sensors (Basel, Switzerland)
Human-vehicle classification is an essential component to avoiding accidents in autonomous driving. The classification technique based on the automotive radar sensor has been paid more attention by related researchers, owing to its robustness to low-...

Deep Learning-Based Human Activity Real-Time Recognition for Pedestrian Navigation.

Sensors (Basel, Switzerland)
Several pedestrian navigation solutions have been proposed to date, and most of them are based on smartphones. Real-time recognition of pedestrian mode and smartphone posture is a key issue in navigation. Traditional ML (Machine Learning) classificat...

Doppler-Spectrum Feature-Based Human-Vehicle Classification Scheme Using Machine Learning for an FMCW Radar Sensor.

Sensors (Basel, Switzerland)
In this paper, we propose a Doppler-spectrum feature-based human-vehicle classification scheme for an FMCW (frequency-modulated continuous wave) radar sensor. We introduce three novel features referred to as the scattering point count, scattering poi...

Pedestrian Navigation Method Based on Machine Learning and Gait Feature Assistance.

Sensors (Basel, Switzerland)
In recent years, as the mechanical structure of humanoid robots increasingly resembles the human form, research on pedestrian navigation technology has become of great significance for the development of humanoid robot navigation systems. To solve th...