318,113 research outputs found
Multiple Object Tracking
Práce se zabývá teoretickým popisem, návrhem a implementací systému pro sledování objektů v zorném poli kamery pomocí jednoduché webkamery. Při zpracování obrazových dat je použitá knihovna OpenCV. Systém je schopný řídit otáčení kamery tak, aby uživatelem vybraný objekt byl v středu obrazu. Pro řízení kamery je využívána řídící deska FITkit. Program je implementován v jazyce C++.This paper is focusing on technical specification, concept and implementation of multiple object tracking with using a standard web camera. Image data processing has been done through the OpanCV library. The system is able to rotate the camera, in order to have the chosen target in the center of the screen. FITkit controller has been used to control the camera. The software has been implemented in the C++ environment.
Object-Centric Multiple Object Tracking
Unsupervised object-centric learning methods allow the partitioning of scenes
into entities without additional localization information and are excellent
candidates for reducing the annotation burden of multiple-object tracking (MOT)
pipelines. Unfortunately, they lack two key properties: objects are often split
into parts and are not consistently tracked over time. In fact,
state-of-the-art models achieve pixel-level accuracy and temporal consistency
by relying on supervised object detection with additional ID labels for the
association through time. This paper proposes a video object-centric model for
MOT. It consists of an index-merge module that adapts the object-centric slots
into detection outputs and an object memory module that builds complete object
prototypes to handle occlusions. Benefited from object-centric learning, we
only require sparse detection labels (0%-6.25%) for object localization and
feature binding. Relying on our self-supervised
Expectation-Maximization-inspired loss for object association, our approach
requires no ID labels. Our experiments significantly narrow the gap between the
existing object-centric model and the fully supervised state-of-the-art and
outperform several unsupervised trackers.Comment: ICCV 2023 camera-ready versio
Instance Flow Based Online Multiple Object Tracking
We present a method to perform online Multiple Object Tracking (MOT) of known
object categories in monocular video data. Current Tracking-by-Detection MOT
approaches build on top of 2D bounding box detections. In contrast, we exploit
state-of-the-art instance aware semantic segmentation techniques to compute 2D
shape representations of target objects in each frame. We predict position and
shape of segmented instances in subsequent frames by exploiting optical flow
cues. We define an affinity matrix between instances of subsequent frames which
reflects locality and visual similarity. The instance association is solved by
applying the Hungarian method. We evaluate different configurations of our
algorithm using the MOT 2D 2015 train dataset. The evaluation shows that our
tracking approach is able to track objects with high relative motions. In
addition, we provide results of our approach on the MOT 2D 2015 test set for
comparison with previous works. We achieve a MOTA score of 32.1
Hierarchical fuzzy logic based approach for object tracking
In this paper a novel tracking approach based on fuzzy concepts is introduced. A methodology for both single and multiple object tracking is presented. The aim of this methodology is to use these concepts as a tool to, while maintaining the needed accuracy, reduce the complexity usually involved in object tracking problems. Several dynamic fuzzy sets are constructed according to both kinematic and non-kinematic properties that distinguish the object to be tracked. Meanwhile kinematic related fuzzy sets model the object's motion pattern, the non-kinematic fuzzy sets model the object's appearance. The tracking task is performed through the fusion of these fuzzy models by means of an inference engine. This way, object detection and matching steps are performed exclusively using inference rules on fuzzy sets. In the multiple object methodology, each object is associated with a confidence degree and a hierarchical implementation is performed based on that confidence degree.info:eu-repo/semantics/publishedVersio
Extended Object Tracking: Introduction, Overview and Applications
This article provides an elaborate overview of current research in extended
object tracking. We provide a clear definition of the extended object tracking
problem and discuss its delimitation to other types of object tracking. Next,
different aspects of extended object modelling are extensively discussed.
Subsequently, we give a tutorial introduction to two basic and well used
extended object tracking approaches - the random matrix approach and the Kalman
filter-based approach for star-convex shapes. The next part treats the tracking
of multiple extended objects and elaborates how the large number of feasible
association hypotheses can be tackled using both Random Finite Set (RFS) and
Non-RFS multi-object trackers. The article concludes with a summary of current
applications, where four example applications involving camera, X-band radar,
light detection and ranging (lidar), red-green-blue-depth (RGB-D) sensors are
highlighted.Comment: 30 pages, 19 figure
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