192,056 research outputs found
Joint Detection and Tracking in Videos with Identification Features
Recent works have shown that combining object detection and tracking tasks,
in the case of video data, results in higher performance for both tasks, but
they require a high frame-rate as a strict requirement for performance. This is
assumption is often violated in real-world applications, when models run on
embedded devices, often at only a few frames per second.
Videos at low frame-rate suffer from large object displacements. Here
re-identification features may support to match large-displaced object
detections, but current joint detection and re-identification formulations
degrade the detector performance, as these two are contrasting tasks. In the
real-world application having separate detector and re-id models is often not
feasible, as both the memory and runtime effectively double.
Towards robust long-term tracking applicable to reduced-computational-power
devices, we propose the first joint optimization of detection, tracking and
re-identification features for videos. Notably, our joint optimization
maintains the detector performance, a typical multi-task challenge. At
inference time, we leverage detections for tracking (tracking-by-detection)
when the objects are visible, detectable and slowly moving in the image. We
leverage instead re-identification features to match objects which disappeared
(e.g. due to occlusion) for several frames or were not tracked due to fast
motion (or low-frame-rate videos). Our proposed method reaches the
state-of-the-art on MOT, it ranks 1st in the UA-DETRAC'18 tracking challenge
among online trackers, and 3rd overall.Comment: Accepted at Image and Vision Computing Journa
Artificial Intelligence and Systems Theory: Applied to Cooperative Robots
This paper describes an approach to the design of a population of cooperative
robots based on concepts borrowed from Systems Theory and Artificial
Intelligence. The research has been developed under the SocRob project, carried
out by the Intelligent Systems Laboratory at the Institute for Systems and
Robotics - Instituto Superior Tecnico (ISR/IST) in Lisbon. The acronym of the
project stands both for "Society of Robots" and "Soccer Robots", the case study
where we are testing our population of robots. Designing soccer robots is a
very challenging problem, where the robots must act not only to shoot a ball
towards the goal, but also to detect and avoid static (walls, stopped robots)
and dynamic (moving robots) obstacles. Furthermore, they must cooperate to
defeat an opposing team. Our past and current research in soccer robotics
includes cooperative sensor fusion for world modeling, object recognition and
tracking, robot navigation, multi-robot distributed task planning and
coordination, including cooperative reinforcement learning in cooperative and
adversarial environments, and behavior-based architectures for real time task
execution of cooperating robot teams
The Treachery of Images: Bayesian Scene Keypoints for Deep Policy Learning in Robotic Manipulation
In policy learning for robotic manipulation, sample efficiency is of
paramount importance. Thus, learning and extracting more compact
representations from camera observations is a promising avenue. However,
current methods often assume full observability of the scene and struggle with
scale invariance. In many tasks and settings, this assumption does not hold as
objects in the scene are often occluded or lie outside the field of view of the
camera, rendering the camera observation ambiguous with regard to their
location. To tackle this problem, we present BASK, a Bayesian approach to
tracking scale-invariant keypoints over time. Our approach successfully
resolves inherent ambiguities in images, enabling keypoint tracking on
symmetrical objects and occluded and out-of-view objects. We employ our method
to learn challenging multi-object robot manipulation tasks from wrist camera
observations and demonstrate superior utility for policy learning compared to
other representation learning techniques. Furthermore, we show outstanding
robustness towards disturbances such as clutter, occlusions, and noisy depth
measurements, as well as generalization to unseen objects both in simulation
and real-world robotic experiments
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
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