2,138 research outputs found
Track, then Decide: Category-Agnostic Vision-based Multi-Object Tracking
The most common paradigm for vision-based multi-object tracking is
tracking-by-detection, due to the availability of reliable detectors for
several important object categories such as cars and pedestrians. However,
future mobile systems will need a capability to cope with rich human-made
environments, in which obtaining detectors for every possible object category
would be infeasible. In this paper, we propose a model-free multi-object
tracking approach that uses a category-agnostic image segmentation method to
track objects. We present an efficient segmentation mask-based tracker which
associates pixel-precise masks reported by the segmentation. Our approach can
utilize semantic information whenever it is available for classifying objects
at the track level, while retaining the capability to track generic unknown
objects in the absence of such information. We demonstrate experimentally that
our approach achieves performance comparable to state-of-the-art
tracking-by-detection methods for popular object categories such as cars and
pedestrians. Additionally, we show that the proposed method can discover and
robustly track a large variety of other objects.Comment: ICRA'18 submissio
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
Applications of a Graph Theoretic Based Clustering Framework in Computer Vision and Pattern Recognition
Recently, several clustering algorithms have been used to solve variety of
problems from different discipline. This dissertation aims to address different
challenging tasks in computer vision and pattern recognition by casting the
problems as a clustering problem. We proposed novel approaches to solve
multi-target tracking, visual geo-localization and outlier detection problems
using a unified underlining clustering framework, i.e., dominant set clustering
and its extensions, and presented a superior result over several
state-of-the-art approaches.Comment: doctoral dissertatio
Novel Aggregated Solutions for Robust Visual Tracking in Traffic Scenarios
This work proposes novel approaches for object tracking in challenging scenarios like severe occlusion, deteriorated vision and long range multi-object reidentification. All these solutions are only based on image sequence captured by a monocular camera and do not require additional sensors. Experiments on standard benchmarks demonstrate an improved state-of-the-art performance of these approaches. Since all the presented approaches are smartly designed, they can run at a real-time speed
- …