Abstract
This paper proposes a novel architecture for real-time 3D ball trajectory estimation with a monocular camera in Middle Size League scenario. Our proposed system consists on detecting possible multiple ball candidates in the image, that are filtered in a multi-target data association layer. Validated ball candidates have their 3D trajectory estimated by Maximum Likelihood method (MLM) followed by a recursive refinement obtained with an Extended Kalman Filter (EKF). Our approach was validated in real RoboCup scenario, evaluated recurring to ground truth information obtained by alternative methods allowing overall performance and quality assessment. © 2012 Springer-Verlag Berlin Heidelberg.

Abstract
In this work a forest fire detection solution using small autonomous aerial vehicles is proposed. The FALCOS unmanned aerial vehicle developed for remote-monitoring purposes is described. This is a small size UAV with onboard vision processing and autonomous flight capabilities. A set of custom developed navigation sensors was developed for the vehicle. Fire detection is performed through the use of low cost digital cameras and near-infrared sensors. Test results for navigation and ignition detection in real scenario are presented.

Abstract
This paper propose a real-time vision framework for mobile robotics and describes the current implementation. The pipeline structure further reduces latency and allows a paralleled hardware implementation. A dedicated hardware vision sensor was developed in order to take advantage of the proposed architecture. The real-time characteristics and hardware partial implementation, coupled with low energy consumption address typical autonomous systems applications. A characterization of the implemented system in the Robocup scenario, during competition matches, is presented.

Abstract
Marine litter is a growing environmental threat, with severe ecological and socio-economic impacts. Most monitoring strategies rely on optical sensors to detect surface pollution, however these approaches fail to capture submerged plastics dispersed throughout the water column. Multibeam acoustic imaging offers a complementary solution, but the scarcity of annotated sonar datasets and the high noise levels of acoustic imagery make automated detection and tracking particularly challenging. This study presents a comparative evaluation of deep learning based multi-object tracking (MOT) algorithms applied to water column acoustic data. Pre-trained YOLOv8 detectors were integrated with tracking-by-detection frameworks including BoT-SORT, OC-SORT, ByteTrack, and DeepOC-SORT. Performance was assessed across acoustic frequencies and preprocessing strategies using standard MOT metrics. Results show that adaptive Gaussian thresholding and opening morphology improved robustness at lower frequencies ( 950 kHz and 1200 kHz ), while unprocessed inputs proved more resilient to severe clutter at 1400 kHz . BoostTrack and ByteTrack achieved the most consistent tracking, effectively managing intermittent detections to maximise MOTA and IDF1. In contrast, OC-SORT underperformed, struggling with fragmented sonar trajectories. Furthermore, while efficient Nano models dominated at lower frequencies, Medium models were required under higher noise. These findings demonstrate the feasibility of applying MOT methods to sonar-based litter monitoring. Future work will explore unsupervised learning approaches to leverage intrinsic sonar data structure, reduce annotation needs, and enable scalable marine litter tracking.

Abstract