15,518 research outputs found
UA-DETRAC: A New Benchmark and Protocol for Multi-Object Detection and Tracking
In recent years, numerous effective multi-object tracking (MOT) methods are
developed because of the wide range of applications. Existing performance
evaluations of MOT methods usually separate the object tracking step from the
object detection step by using the same fixed object detection results for
comparisons. In this work, we perform a comprehensive quantitative study on the
effects of object detection accuracy to the overall MOT performance, using the
new large-scale University at Albany DETection and tRACking (UA-DETRAC)
benchmark dataset. The UA-DETRAC benchmark dataset consists of 100 challenging
video sequences captured from real-world traffic scenes (over 140,000 frames
with rich annotations, including occlusion, weather, vehicle category,
truncation, and vehicle bounding boxes) for object detection, object tracking
and MOT system. We evaluate complete MOT systems constructed from combinations
of state-of-the-art object detection and object tracking methods. Our analysis
shows the complex effects of object detection accuracy on MOT system
performance. Based on these observations, we propose new evaluation tools and
metrics for MOT systems that consider both object detection and object tracking
for comprehensive analysis.Comment: 18 pages, 11 figures, accepted by CVI
Learning Robust Object Recognition Using Composed Scenes from Generative Models
Recurrent feedback connections in the mammalian visual system have been
hypothesized to play a role in synthesizing input in the theoretical framework
of analysis by synthesis. The comparison of internally synthesized
representation with that of the input provides a validation mechanism during
perceptual inference and learning. Inspired by these ideas, we proposed that
the synthesis machinery can compose new, unobserved images by imagination to
train the network itself so as to increase the robustness of the system in
novel scenarios. As a proof of concept, we investigated whether images composed
by imagination could help an object recognition system to deal with occlusion,
which is challenging for the current state-of-the-art deep convolutional neural
networks. We fine-tuned a network on images containing objects in various
occlusion scenarios, that are imagined or self-generated through a deep
generator network. Trained on imagined occluded scenarios under the object
persistence constraint, our network discovered more subtle and localized image
features that were neglected by the original network for object classification,
obtaining better separability of different object classes in the feature space.
This leads to significant improvement of object recognition under occlusion for
our network relative to the original network trained only on un-occluded
images. In addition to providing practical benefits in object recognition under
occlusion, this work demonstrates the use of self-generated composition of
visual scenes through the synthesis loop, combined with the object persistence
constraint, can provide opportunities for neural networks to discover new
relevant patterns in the data, and become more flexible in dealing with novel
situations.Comment: Accepted by 14th Conference on Computer and Robot Visio
Physical simulation for monocular 3D model based tracking
The problem of model-based object tracking in three dimensions is addressed. Most previous work on tracking assumes simple motion models, and consequently tracking typically fails in a variety of situations. Our insight is that incorporating physics models of object behaviour improves tracking performance in these cases. In particular it allows us to handle tracking in the face of rigid body interactions where there is also occlusion and fast object motion. We show how to incorporate rigid body physics simulation into a particle filter. We present two methods for this based on pose and force noise. The improvements are tested on four videos of a robot pushing an object, and results indicate that our approach performs considerably better than a plain particle filter tracker, with the force noise method producing the best results over the range of test videos
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
- …