Context plays an important role in general scene perception. In particular, it can provide cues about an object’s location within an image. In computer vision, object detectors typically ignore this information. We tackle this problem by presenting a concept of how to extract and learn contextual information from examples. This context is then used to calculate a focus of attention, that repres...

BACKGROUND Pedestrian injuries among older people tend to occur near their residence. However, knowledge regarding whether distance travelled from home to the injury site or road environmental/socioeconomic factors affect injury severity remains limited. METHODS A cross-sectional study was performed using injury registry data from the Kurume City Fire Department, Japan. Distance travelled fro...

Insight into walking behavior is essential for theory and model development describing the behavior of individual pedestrians. In turn, microscopic pedestrian simulation models can be used to test and compare different infrastructure designs, both from the perspective of efficiency and safety. To calibrate these microscopic models, detailed data is required. However, currently such data is not ...

We present a new hierarchical event detection approach for highly complex scenarios in pedestrian groups on the basis of airborne image sequences from UAVs. Related work on event detection for pedestrians is capable of learning and analyzing recurring motion paths to detect abnormal paths and of analyzing the type of motion interaction between pairs of pedestrians. However, these approaches can...

INTRODUCTION This study examines the effects of high-visibility garment design on daytime pedestrian conspicuity in work zones. Factors assessed were garment color, amount of background material, pedestrian arm motion, scene complexity, and driver age. METHOD The study was conducted in naturalistic conditions on public roads in real traffic. Drivers drove two passes on a 31-km route and indic...

Object detections are critical technologies for the safety of pedestrians and drivers in autonomous vehicles. Above all, occluded pedestrian detection is still a challenging topic. We propose a new detection scheme for occluded pedestrian detection by means of lidar–radar sensor fusion. In the proposed method, the lidar and radar regions of interest (RoIs) have been selected based on the respec...

State-of-the-art methods treat pedestrian attribute recognition as a multi-label image classification problem. The location information of person attributes is usually eliminated or simply encoded in the rigid splitting of whole body in previous work. In this paper, we formulate the task in a weakly-supervised attribute localization framework. Based on GoogLeNet, firstly, a set of mid-level att...

Random Forest (RF) is a successful paradigm for learning classifiers due to its ability to learn from large feature spaces and seamlessly integrate multi-class classification, as well as the achieved accuracy and processing efficiency. However, as many other classifiers, RF requires domain adaptation (DA) provided that there is a mismatch between the training (source) and testing (target) domai...

Human Detection in Images is a contemporary Computer Vision problem, still welcoming improved solutions. This subset area of object detection has seen many attempts made towards efficient implementation and in this project proposal we describe one based on Histogram of Oriented Gradients which proves to be superior than the rest in terms of both Detection rate and Error rate when using a Linear...

Fusion of laser and vision in object detection has been accomplished by two main approaches: (1) independent integration of sensor-driven features or sensor-driven classifiers, or (2) a region of interest (ROI) is found by laser segmentation and an image classifier is used to name the projected ROI. Here, we propose a novel fusion approach based on semantic information, and embodied on many lev...