1,269 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
MOTChallenge: A Benchmark for Single-Camera Multiple Target Tracking
Standardized benchmarks have been crucial in pushing the performance of
computer vision algorithms, especially since the advent of deep learning.
Although leaderboards should not be over-claimed, they often provide the most
objective measure of performance and are therefore important guides for
research. We present MOTChallenge, a benchmark for single-camera Multiple
Object Tracking (MOT) launched in late 2014, to collect existing and new data,
and create a framework for the standardized evaluation of multiple object
tracking methods. The benchmark is focused on multiple people tracking, since
pedestrians are by far the most studied object in the tracking community, with
applications ranging from robot navigation to self-driving cars. This paper
collects the first three releases of the benchmark: (i) MOT15, along with
numerous state-of-the-art results that were submitted in the last years, (ii)
MOT16, which contains new challenging videos, and (iii) MOT17, that extends
MOT16 sequences with more precise labels and evaluates tracking performance on
three different object detectors. The second and third release not only offers
a significant increase in the number of labeled boxes but also provide labels
for multiple object classes beside pedestrians, as well as the level of
visibility for every single object of interest. We finally provide a
categorization of state-of-the-art trackers and a broad error analysis. This
will help newcomers understand the related work and research trends in the MOT
community, and hopefully shed some light on potential future research
directions.Comment: Accepted at IJC
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