Sökning: "multi object tracking"

Visar resultat 1 - 5 av 27 uppsatser innehållade orden multi object tracking.

1. 1. Smart Tracking for Edge-assisted Object Detection : Deep Reinforcement Learning for Multi-objective Optimization of Tracking-based Detection Process

   Master-uppsats, KTH/Skolan för elektroteknik och datavetenskap (EECS)

   Författare :Shihang Zhou; [2023]
   Nyckelord :Tracking-By-Detection; Deep Reinforcement Learning; Multi-Objective Optimization; Spårning genom detektion; Djup förstärkningsinlärning; Multiobjektiv optimering;

   Sammanfattning : Detecting generic objects is one important sensing task for applications that need to understand the environment, for example eXtended Reality (XR), drone navigation etc. However, Object Detection algorithms are particularly computationally heavy for real-time video analysis on resource-constrained mobile devices. LÄS MER

3. 2. Probabilistic Multi-Modal Data Fusion and Precision Coordination for Autonomous Mobile Systems Navigation : A Predictive and Collaborative Approach to Visual-Inertial Odometry in Distributed Sensor Networks using Edge Nodes

   Master-uppsats, KTH/Skolan för elektroteknik och datavetenskap (EECS)

   Författare :Isabella Luppi; [2023]
   Nyckelord :Distributed Sensor Networks; Point Cloud Processing; Bounding Box Fitting; Trajectory Tracking; Distributed Estimation; Predictive Estimation; Edge-Computing; Reti di Sensori Distribuiti; Elaborazione di Nuvole di Punti; Riquadri di Delimitazione; Tracciamento della Traiettoria; Stima Distribuita; Stima Predittiva; Calcolo Distribuito.; Distribuerade Sensornätverk; Bearbetning av Punktmoln; Anpassning av Begränsningsruta; Trajektorieuppföljning; Distribuerad Uppskattning; Prediktiv Uppskattning; Edge-datorbehandling;

   Sammanfattning : This research proposes a novel approach for improving autonomous mobile system navigation in dynamic and potentially occluded environments. The research introduces a tracking framework that combines data from stationary sensing units and on-board sensors, addressing challenges of computational efficiency, reliability, and scalability. LÄS MER

4. 3. A Bidirectional ApproachApplied on Deeper and WiderSiamese Network

   Master-uppsats, Uppsala universitet/Institutionen för informationsteknologi

   Författare :Tobias Arnehall Johansson; [2023]
   Nyckelord :;

   Sammanfattning : Object tracking and object detection are two components within computer vision that have been widely improved during the last decade, in terms of precision and speed. This is mainly because deep learning has been incorporatedinto the algorithms, but also because new techniques and insights within the area are frequently released. LÄS MER

5. 4. Analyzing different approaches to Visual SLAM in dynamic environments : A comparative study with focus on strengths and weaknesses

   Master-uppsats, KTH/Skolan för elektroteknik och datavetenskap (EECS)

   Författare :Kristín Sól Ólafsdóttir; [2023]
   Nyckelord :Visual SLAM; RGB-D Vision; Dynamic Objects; Object Detection; Multi-Object Tracking; Image Segmentation; Optical Flow; Visual SLAM; RGB-D Syn; Dynamiska objekt; Objektdetektering; Multi-Objekt Spårning; Bildsegmentation; Optiskt Flöde;

   Sammanfattning : Simultaneous Localization and Mapping (SLAM) is the crucial ability for many autonomous systems to operate in unknown environments. In recent years SLAM development has focused on achieving robustness regarding the challenges the field still faces e.g. dynamic environments. LÄS MER

7. 5. Multi-Camera Multi-Person Tracking Using Reinforcement Learning

   Master-uppsats, Lunds universitet/Matematik LTH

   Författare :Axel Kärrholm; Linus Rickman; [2022]
   Nyckelord :Multi-Camera Multi-Object Tracking; Reinforcement Learning; Computer Vision; Machine Learning; Object Detection; Mathematics and Statistics;

   Sammanfattning : The problem of multi-object-tracking in a network of cameras is an interesting and non-trivial problem. Given videos from a number of cameras the goal of Multi-Camera Multi-Object Tracking (MCMOT) is to find the full visible trajectory of each pedestrian from the videos as the pedestrians move across cameras. LÄS MER