1,279 research outputs found
Comparison of fusion methods for thermo-visual surveillance tracking
In this paper, we evaluate the appearance tracking performance of multiple fusion schemes that combine information from standard CCTV and thermal infrared spectrum video for the tracking of surveillance objects, such as people, faces, bicycles and vehicles. We show results on numerous real world multimodal surveillance sequences, tracking challenging objects whose appearance changes rapidly. Based on these results we can determine the most promising fusion scheme
Pedestrian Detection and Tracking in Video Surveillance System: Issues, Comprehensive Review, and Challenges
Pedestrian detection and monitoring in a surveillance system are critical for numerous utility areas which encompass unusual event detection, human gait, congestion or crowded vicinity evaluation, gender classification, fall detection in elderly humans, etc. Researchers’ primary focus is to develop surveillance system that can work in a dynamic environment, but there are major issues and challenges involved in designing such systems. These challenges occur at three different levels of pedestrian detection, viz. video acquisition, human detection, and its tracking. The challenges in acquiring video are, viz. illumination variation, abrupt motion, complex background, shadows, object deformation, etc. Human detection and tracking challenges are varied poses, occlusion, crowd density area tracking, etc. These results in lower recognition rate. A brief summary of surveillance system along with comparisons of pedestrian detection and tracking technique in video surveillance is presented in this chapter. The publicly available pedestrian benchmark databases as well as the future research directions on pedestrian detection have also been discussed
Visual Clutter Study for Pedestrian Using Large Scale Naturalistic Driving Data
Some of the pedestrian crashes are due to driver’s late or difficult perception of pedestrian’s appearance. Recognition of pedestrians during driving is a complex cognitive activity. Visual clutter analysis can be used to study the factors that affect human visual search efficiency and help design advanced driver assistant system for better decision making and user experience. In this thesis, we propose the pedestrian perception evaluation model which can quantitatively analyze the pedestrian perception difficulty using naturalistic driving data. An efficient detection framework was developed to locate pedestrians within large scale naturalistic driving data. Visual clutter analysis was used to study the factors that may affect the driver’s ability to perceive pedestrian appearance. The candidate factors were explored by the designed exploratory study using naturalistic driving data and a bottom-up image-based pedestrian clutter metric was proposed to quantify the pedestrian perception difficulty in naturalistic driving data. Based on the proposed bottom-up clutter metrics and top-down pedestrian appearance based estimator, a Bayesian probabilistic pedestrian perception evaluation model was further constructed to simulate the pedestrian perception process
Agreeing to Cross: How Drivers and Pedestrians Communicate
The contribution of this paper is twofold. The first is a novel dataset for
studying behaviors of traffic participants while crossing. Our dataset contains
more than 650 samples of pedestrian behaviors in various street configurations
and weather conditions. These examples were selected from approx. 240 hours of
driving in the city, suburban and urban roads. The second contribution is an
analysis of our data from the point of view of joint attention. We identify
what types of non-verbal communication cues road users use at the point of
crossing, their responses, and under what circumstances the crossing event
takes place. It was found that in more than 90% of the cases pedestrians gaze
at the approaching cars prior to crossing in non-signalized crosswalks. The
crossing action, however, depends on additional factors such as time to
collision (TTC), explicit driver's reaction or structure of the crosswalk.Comment: 6 pages, 6 figure
Near-field Perception for Low-Speed Vehicle Automation using Surround-view Fisheye Cameras
Cameras are the primary sensor in automated driving systems. They provide
high information density and are optimal for detecting road infrastructure cues
laid out for human vision. Surround-view camera systems typically comprise of
four fisheye cameras with 190{\deg}+ field of view covering the entire
360{\deg} around the vehicle focused on near-field sensing. They are the
principal sensors for low-speed, high accuracy, and close-range sensing
applications, such as automated parking, traffic jam assistance, and low-speed
emergency braking. In this work, we provide a detailed survey of such vision
systems, setting up the survey in the context of an architecture that can be
decomposed into four modular components namely Recognition, Reconstruction,
Relocalization, and Reorganization. We jointly call this the 4R Architecture.
We discuss how each component accomplishes a specific aspect and provide a
positional argument that they can be synergized to form a complete perception
system for low-speed automation. We support this argument by presenting results
from previous works and by presenting architecture proposals for such a system.
Qualitative results are presented in the video at https://youtu.be/ae8bCOF77uY.Comment: Accepted for publication at IEEE Transactions on Intelligent
Transportation System
Scale-adaptive spatial appearance feature density approximation for object tracking
Object tracking is an essential task in visual traffic surveillance. Ideally, a tracker should be able to accurately capture an object's natural motion such as translation, rotation, and scaling. However, it is well known that object appearance varies due to changes in viewing angle, scale, and illumination. They introduce ambiguity to the image cue on which a visual tracker usually relies and which affects the tracking performance. Thus, a robust image appearance cue is required. This paper proposes scale-adaptive spatial appearance feature density approximation to represent objects and construct the image cue. It is found that the appearance representation improves the sensitivity on both the object's rotation and scale. The image cue is then constructed by both the appearance representation of the object and its surrounding background such that distinguishable parts of an object can be tracked under poor imaging conditions. Moreover, tracking dynamics is integrated with the image cue so that objects are efficiently localized in a gradient-based process. Comparative experiments show that the proposed method is effective in capturing the natural motion of objects and generating better tracking accuracy under different image conditions. © 2010 IEEE.published_or_final_versio
- …