In this paper, we provide a survey of the traffic sign detection literature, detailing systems for recognition (TSR) driver assistance. We separately describe contributions recent works to various stages inherent in detection: segmentation, feature extraction, and final detection. While TSR is well-established research area, highlight open issues including dearth use publicly available image databases over-representation European signs. Furthermore, discuss future directions research,...

This paper presents the challenges that researchers must overcome in traffic light recognition (TLR) research and provides an overview of ongoing work. The aim is to elucidate which areas have been thoroughly researched not, thereby uncovering opportunities for further improvement. An applied methods noteworthy contributions from a wide range recent papers presented, along with corresponding evaluation results. TLR systems studied discussed depth, we propose common procedure, will strengthen...

Detecting pedestrians is still a challenging task for automotive vision systems due to the extreme variability of targets, lighting conditions, occlusion, and high-speed vehicle motion. Much research has been focused on this problem in last ten years detectors based classifiers have gained special place among different approaches presented. This paper presents state-of-the-art pedestrian detection system two-stage classifier. Candidates are extracted with Haar cascade classifier trained...

This paper presents a comprehensive research study of the detection US traffic signs.Until now, in Traffic Sign Recognition systems has been centered on European signs, but signs can look very different across parts world, and system which works well Europe may indeed not work US.We go over recent advances sign discuss differences world.Then we present extension to publicly available LISA-TS dataset, almost doubling its size, now with HD-quality footage.The is made testing tracking mind,...

Traffic light recognition (TLR) is an integral part of any intelligent vehicle, which must function in the existing infrastructure. Pedestrian and sign detection have recently seen great improvements due to introduction learning based detectors using channel features. A similar push not yet been for sub-problem TLR, where dominated by methods on heuristic models. Evaluation systems currently limited primarily small local datasets. In order provide a common basis comparing future TLR research...

This paper presents a monocular and purely vision based pedestrian trajectory tracking prediction framework with integrated map-based hazard inference. In Advanced Driver Assistance systems research, lot of effort has been put into detection over the last decade, several are indeed showing impressive results. Considerably less processing detections further. We present system for pedestrians, which on bounding boxes tracks pedestrians is able to predict their positions in near future. The...

Person re-identification is about recognizing people who have passed by a sensor earlier. Previous work mainly based on RGB data, but in this we for the first time present system where combine RGB, depth, and thermal data purposes. First, from each of three modalities, obtain some particular features: model color information different regions body, depth compute soft body biometrics, extract local structural information. Then, types are combined joined classifier. The tri-modal evaluated new...

Accurate assessment of forest biodiversity is crucial for ecosystem management and conservation. While traditional field surveys provide high-quality assessments, they are labor-intensive spatially limited. This study investigates whether deep learning-based fusion close-range sensing data from 2D orthophotos (12.5 cm resolution) 3D airborne laser scanning (ALS) point clouds (8 points/m^2) can enhance assessment. We introduce the BioVista dataset, comprising 44.378 paired samples ALS...

Traffic sign detection is crucial in intelligent vehicles, no matter if one's objective to develop Advanced Driver Assistance Systems or autonomous cars. Recent advances traffic detection, especially the great effort put into competition German Sign Detection Benchmark, have given rise very reliable systems when tested on European signs. The U.S., however, has a rather different approach design. This paper evaluates whether current state-of-the-art detector useful for American We find that...

Active learning strategies for 3D object detection in autonomous driving datasets may help to address challenges of data imbalance, redundancy, and high-dimensional data. We demonstrate the effectiveness entropy querying select informative samples, aiming reduce annotation costs improve model performance. experiment using BEVFusion on nuScenes dataset, comparing active random sampling demonstrating that outperforms most cases. The method is particularly effective reducing performance gap...

This paper presents a novel convolutional neural network (CNN)-based traffic sign recognition system and investigates pre- post-processing methods for enhancing performance. We focus on speed limit signs, the most difficult superclass in US set. The Cuda-convnet is chosen as suitable model task with low-resolution training images limited dataset size. test world's largest public of LISA-TS extension, testing dataset. Compared current state-of-the-art aggregated channel features detector that...

This paper introduces a system for estimating the attention of driver wearing first person view camera using salient objects to improve gaze estimation. A challenging data set pedestrians crossing intersections has been captured Google Glass worn by driver. challenge unique from cars is that interior car can take up large part image. The proposed automatically filters out dashboard car, along with other parts instrumentation. remaining area used as region interest pedestrian detector. Two...

Object detection is crucial for ensuring safe autonomous driving. However, data-driven approaches face challenges when encountering minority or novel objects in the 3D driving scene. In this paper, we propose VisLED, a language-driven active learning framework diverse open-set Detection. Our method leverages techniques to query and informative data samples from an unlabeled pool, enhancing model's ability detect underrepresented objects. Specifically, introduce Vision-Language Embedding...

Video Foundation Models (ViFMs) aim to develop general-purpose representations for various video understanding tasks by leveraging large-scale datasets and powerful models capture robust generic features from data.This survey analyzes over 200 methods, offering a comprehensive overview of benchmarks evaluation metrics across 15 distinct tasks, categorized into three main groups.Additionally, we provide an in-depth performance analysis these the six most common tasks.We identify approaches...

Video Foundation Models (ViFMs) aim to learn a general-purpose representation for various video understanding tasks.Leveraging large-scale datasets and powerful models, ViFMs achieve this by capturing robust generic features from data.This survey analyzes over 200 foundational offering comprehensive overview of benchmarks evaluation metrics across 14 distinct tasks categorized into 3 main categories.Additionally, we offer an in-depth performance analysis these models the 6 most common...

This paper describes a system for person re-identification using RGB-D sensors. The covers the full flow, from detection of subjects, over contour extraction, to soft biometrics. biometrics in question are part-based color histograms and subjects height. Subjects added transient database re-identified based on distance between recorded currently measured metrics. works live video requires no collaboration subjects. achieves 68% rate with wrong re-identifications, result that compares...

This paper proposes the use of multiple low-cost visual sensors to obtain a surround view ego-vehicle for semantic understanding. A multi-perspective will assist analysis naturalistic driving studies (NDS), by automating task data reduction observed sequences into events. user-centric vision-based framework is presented using vehicle detector and tracker in each separate perspective. Multi-perspective trajectories are estimated analyzed extract 14 different events, including potential...

Traffic sign recognition (TSR) is a research field that has seen much activity in the recent decade. This paper introduces problem and presents 4 papers on traffic detection classification. It attempts to extract trends touch upon unexplored areas, especially lack of into integrating TSR with driver-in-the-loop system some problems presents. an exciting great promises for integration driver assistance systems particular area deserves be explored further.

This paper introduces a part-based two-stage pedestrian detector. The system finds candidates with an AdaBoost cascade on Haar-like features. It then verifies each candidate using HOG-SVM doing first regression and classification based the estimated function output from regression. uses Histogram of Oriented Gradients (HOG) computed both full, upper lower body candidates, these in final verification. has been trained tested INRIA dataset performs better than similar previous work, which full-body