10 research outputs found
Non-Causal Tracking by Deblatting
Tracking by Deblatting stands for solving an inverse problem of deblurring
and image matting for tracking motion-blurred objects. We propose non-causal
Tracking by Deblatting which estimates continuous, complete and accurate object
trajectories. Energy minimization by dynamic programming is used to detect
abrupt changes of motion, called bounces. High-order polynomials are fitted to
segments, which are parts of the trajectory separated by bounces. The output is
a continuous trajectory function which assigns location for every real-valued
time stamp from zero to the number of frames. Additionally, we show that from
the trajectory function precise physical calculations are possible, such as
radius, gravity or sub-frame object velocity. Velocity estimation is compared
to the high-speed camera measurements and radars. Results show high performance
of the proposed method in terms of Trajectory-IoU, recall and velocity
estimation.Comment: Published at GCPR 2019, oral presentation, Best Paper Honorable
Mention Awar
CDTB: A Color and Depth Visual Object Tracking Dataset and Benchmark
A long-term visual object tracking performance evaluation methodology and a
benchmark are proposed. Performance measures are designed by following a
long-term tracking definition to maximize the analysis probing strength. The
new measures outperform existing ones in interpretation potential and in better
distinguishing between different tracking behaviors. We show that these
measures generalize the short-term performance measures, thus linking the two
tracking problems. Furthermore, the new measures are highly robust to temporal
annotation sparsity and allow annotation of sequences hundreds of times longer
than in the current datasets without increasing manual annotation labor. A new
challenging dataset of carefully selected sequences with many target
disappearances is proposed. A new tracking taxonomy is proposed to position
trackers on the short-term/long-term spectrum. The benchmark contains an
extensive evaluation of the largest number of long-term tackers and comparison
to state-of-the-art short-term trackers. We analyze the influence of tracking
architecture implementations to long-term performance and explore various
re-detection strategies as well as influence of visual model update strategies
to long-term tracking drift. The methodology is integrated in the VOT toolkit
to automate experimental analysis and benchmarking and to facilitate future
development of long-term trackers
Unsupervised Green Object Tracker (GOT) without Offline Pre-training
Supervised trackers trained on labeled data dominate the single object
tracking field for superior tracking accuracy. The labeling cost and the huge
computational complexity hinder their applications on edge devices.
Unsupervised learning methods have also been investigated to reduce the
labeling cost but their complexity remains high. Aiming at lightweight
high-performance tracking, feasibility without offline pre-training, and
algorithmic transparency, we propose a new single object tracking method,
called the green object tracker (GOT), in this work. GOT conducts an ensemble
of three prediction branches for robust box tracking: 1) a global object-based
correlator to predict the object location roughly, 2) a local patch-based
correlator to build temporal correlations of small spatial units, and 3) a
superpixel-based segmentator to exploit the spatial information of the target
frame. GOT offers competitive tracking accuracy with state-of-the-art
unsupervised trackers, which demand heavy offline pre-training, at a lower
computation cost. GOT has a tiny model size (<3k parameters) and low inference
complexity (around 58M FLOPs per frame). Since its inference complexity is
between 0.1%-10% of DL trackers, it can be easily deployed on mobile and edge
devices
FuCoLoT – A Fully-Correlational Long-Term Tracker
We propose FuCoLoT – a Fully Correlational Long-term Tracker. It exploits the novel DCF constrained filter learning method to design a detector that is able to re-detect the target in the whole image efficiently. FuCoLoT maintains several correlation filters trained on different time scales that act as the detector components. A novel mechanism based on the correlation response is used for tracking failure estimation. FuCoLoT achieves state-of-the-art results on standard short-term benchmarks and it outperforms the current best-performing tracker on the long-term UAV20L benchmark by over 19%. It has an order of magnitude smaller memory footprint than its best-performing competitors and runs at 15Â fps in a single CPU thread
Vision and Depth Based Computerized Anthropometry and Object Tracking
The thesis has two interconnected parts: Computerized Anthropometry and RGBD (RGB plus Depth) object tracking. In the first part of this thesis, we start from the mathematical representation of the human body shape model. It briefly introduces prior arts from the classic human body models to the latest deep neural network based approaches. We describe the performance metrics and popular datasets for evaluating computerized anthropometry estimation algorithms in a unified setting. The first part of this thesis is about describing our contribution over two aspects of human body anthropometry research: 1) a statistical method for estimating anthropometric measurements from scans, and 2) a deep neural network based solution for learning anthropometric measurements from binary silhouettes. We also release two body shape datasets for accommodating data driven learning methods.
In the second part of this thesis, we explore RGBD object tracking. We start from the current state of RGBD tracking compared to RGB tracking and briefly introduce prior arts from engineered features based methods to deep neural network based methods. We present three deep learning based methods that integrate deep depth features into RGBD object tracking. We also release a unified RGBD tracking benchmark for data driven RGBD tracking algorithms. Finally, we explore RGBD tracking with deep depth features and demonstrate that depth cues significantly benefit the target model learning