Albireo is an adaptive, energy‑efficient inference framework for video object detection on edge devices that wraps existing detectors without modification. It uses a 10‑dimensional Kalman filter per active object to decide when to skip detector calls, predicting bounding boxes on skipped frames at near‑zero GPU cost. Evaluated on BDD100K with YOLO and RF‑DETR detectors on NVIDIA Jetson AGX Thor and Orin, Albireo maintains AP@50 within ±1.2 pp of full‑frame inference while reducing energy consumption by 12.1–17.6 % and improving accuracy for some models.
By Amir Taherin, Jos\'e Cano, Bin Ren, Yanzhi Wang, David Kaeli
arXiv:2603. 24016v2 Announce Type: replace-cross Abstract: Multi-Object Tracking (MOT) has traditionally focused on a few specific categories, restricting its applicability to real-world scenarios involving diverse objects.
By Zekun Qian, Wei Feng, Ruize Han, Junhui Hou
arXiv:2610.26933v1 Announce Type: new
Abstract: An event camera emits events at a rate given by the scene motion, object texture, illumination, its own contrast thresholds, and other settings. Across...
By Kavyansh Tyagi, Andreas Ziegler
arXiv:2608. 10790v1 Announce Type: cross Abstract: Deploying modern video trackers at scale is bottlenecked by the computational cost of RGB-based object detectors.
By I\~naki Erregue, Kamal Nasrollahi, Sergio Escalera
Tetris is a video object tracking system that uses tile-level sampling to efficiently extract high‑fidelity tracks. It partitions videos into tile‑based polyominoes, classifies relevant tiles, prunes redundant ones with an ILP under a user‑defined accuracy constraint, and packs the remaining polyominoes to minimize detector calls. On seven stationary‑video datasets, Tetris maintains less than a 5% loss in tracking accuracy while achieving up to 17.4× higher throughput than prior systems and up to 68.8× higher than a full‑frame reference pipeline.
By Chanwut Kittivorawong, Alena Chao, Charlie Si, Alvin Cheung
arXiv:2609.09396v1 Announce Type: new
Abstract: As Vision-Language Models (VLMs) advance toward physical deployment, the focus has remained on action-oriented Embodied AI evaluated on subject-centric...
By Zaid Pervaiz Bhat, Nimra Nayyar, Arihant Jain, Lap Fung Chan, John Suchanek, Yu Wang, Varun Praveen, Tomasz Kornuta, Vidya Nariyambut Murali
arXiv:2609.17427v1 Announce Type: cross
Abstract: Real-time multi-object tracking systems remain highly vulnerable to full and long-term occlusion, where targets temporarily or completely disappear f...
By Mais Mohammed, Sharifa Mohammed, Hanan Awadh, Haneen Bamaas, Raghad Bawazeer, Elham Alghamdi
A surveillance system that detects an anomaly often has to repair the footage as well, yet the two tasks are studied in isolation: training-free anomaly detectors stop at a score or a label, while tra...
DAPEVO is a learned visual odometry system that independently estimates image and event correspondences at shared patch locations and fuses their correlation evidence before motion refinement. It maintains image and event descriptors for each tracked patch, using a learned scalar gate to combine modality-specific correlation embeddings for each patch–frame edge, followed by a shared recurrent refinement and bundle‑adjustment update. The method supports event‑only observations and modality‑aware keyframe culling, achieving low trajectory error even when RGB frames are sparse or degraded, outperforming DPVO, RAMP‑VO, and event‑only DEVO on UZH‑FPV and TartanEvent datasets.
By Luca Gandolfi, Simone Nascivera, Roberto Pellerito, Rong Zou, Chiara Plizzari, Davide Scaramuzza
The paper investigates how detector architecture, model scale, scene density, and inference regime influence multi‑pedestrian tracking performance. It compares YOLO11, RT‑DETR, and YOLO26 detectors with ByteTrack and BoT‑SORT trackers on MOT17, MOT20, and SOMPT22 datasets, finding RT‑DETR best on HOTA but at higher cost, while YOLO26‑L with NMS balances quality and speed. End‑to‑end inference for YOLO26 did not consistently improve tracking over NMS.
By Pedro Manoel Herminio Alves, Dalton C\'ezane Gomes Valadares, Cl\'audio de Souza Baptista
arXiv:2609.18836v1 Announce Type: new
Abstract: A surveillance system that detects an anomaly often has to repair the footage as well, yet the two tasks are studied in isolation: training-free anomal...
By Zhida Qu, Shengchao Chen
Deploying modern video trackers at scale is bottlenecked by the computational cost of RGB-based object detectors. To this end, we present MVTrack, an ultrafast tracker for moving objects that operates directly on H.