PointEvent introduces serialized motion evidence accumulation for event-based tiny object detection, treating motion continuity as an ordered evidence propagation process. The method organizes event streams into locality‑preserving spatiotemporal paths and chronology‑preserving temporal paths, alternating serialized scans across complementary orders to consolidate fragmented motion evidence. A lightweight event‑wise state‑space framework with a high‑resolution event branch and compact context modulation achieves state‑of‑the‑art performance with the fewest parameters and fastest inference among compared methods.
By Zongze Wu, Baofeng Jia, Weiqi Yan, Jingyuan Zhang, Yu Zang, Xiaoyu Chen, Jing Han
ReWorld-Track introduces a recursive event world model for language‑guided multi‑camera tracking that explicitly carries association uncertainty into future predictions. By treating candidate matches and waiting as alternative target states, the model updates a persistent recurrent belief that preserves uncertainty across successive observations. This approach improves identity continuity and next‑camera accuracy, achieving HOTA scores of 65.19 on CityFlowV2 and 45.36 on MTMMC, and reducing median arrival‑time error from 0.78 s to 0.71 s.
By Haoyang Wu, Shoudong Han, Chaoyue Li, Sijia Chen, Zhenyang Xie, Wang sihan
arXiv:2610.10391v1 Announce Type: new
Abstract: End-to-end multi-object trackers have narrowed the gap with classical tracking-by-detection on association-difficult benchmarks. Yet they still make sp...
By Beno\^it Roussel, Damien Bouet, Liming Chen, Pierre Perrault
End-to-end multi-object trackers have narrowed the gap with classical tracking-by-detection on association-difficult benchmarks. Yet they still make spatially implausible errors no classical tracker w...
TrackEverything is a 3D point tracker that overcomes the trade‑off between sparse long‑horizon tracking and dense short‑clip tracking by representing videos as persistent 3D scene tracks in world coordinates. It introduces voxel‑based de‑duplication at sliding‑window boundaries, a two‑stage refinement process (endpoint refiner and lightweight trajectory refiner), and a 3D WAFT module that replaces memory‑heavy 4D correlation volumes with efficient feature sampling. The method can track all visible points in videos longer than 1000 frames using only 40 GB of GPU memory, outperforming existing dense trackers on short clips and matching sparse trackers on long sequences.
By Ayush Jain, Sreeharsha Paruchuri, Ishita Gupta, Fan Zhang, Tanner Schmidt, Jakob Engel, Katerina Fragkiadaki, Adam W. Harley
The paper introduces MvT, a training‑free multi‑view pipeline for event‑camera based multiple object tracking. MvT preserves the 2‑D event format while generating three complementary spatio‑temporal views, then fuses them into a 3‑D coordinate system to resolve tracking ambiguities that arise in single‑view pseudo‑frames. Experiments on four datasets, including a new Small Objects Dataset, show that MvT outperforms both non‑learning and learning trackers and can be applied when ground‑truth annotations are unavailable.
By Muxi Zha, Banglei Guan, Minzu Liang, Zibin Liu, Yang Shang, Qifeng Yu, Laurent Kneip
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
arXiv:2609.25803v1 Announce Type: new
Abstract: High-rate dense perception in dynamic environments is limited by the low update rate of RGB cameras, as rapid scene changes can occur between frames. E...
By Tao Wan, Xiaoshan Wu, Yifei Yu, Bo Wang, Xiaoyang Lyu, Muxin Liu, Aoxuan Pan, Zhongrui Wang, Xiaojuan Qi
arXiv:2608.22398v1 Announce Type: cross
Abstract: Reliably tracking moving deformable linear objects (DLOs) while simultaneously ensuring robustness, accuracy, and temporally consistent state estimat...
By Annalena Hartmann, Priyamvada Ajithkumar, Patrick Br\"undl, J\"org Franke
arXiv:2610.33356v1 Announce Type: new
Abstract: Event cameras provide microsecond temporal resolution, high dynamic range, and sparse asynchronous measurements, making them compelling for odometry un...
By Matteo Cascone, David Jensen, Alessandro Pirini, Angelo Stefanini, Pietro Ven\`e, Marta Tintor\'e Gazulla, Javier Hidalgo Carri\`o
arXiv:2605.23889v2 Announce Type: replace
Abstract: Online 3D reconstruction requires estimating camera pose and scene geometry under strict causal and bounded-memory constraints. Existing methods of...
By Chong Cheng, Peilin Tao, Nanjie Yao, Guanzhi Ding, Xianda Chen, Yuansen Du, Xiaoyang Guo, Wei Yin, Weiqiang Ren, Qian Zhang, Zhengqing Chen, Hao Wang
The paper introduces PSEE, a method for progressive sensor event expansion that generates pseudo action segments from point-supervised labels in wearable sensor streams. By leveraging semantic activations, sensor-specific transition evidence, and adaptive temporal ownership, PSEE produces high‑quality pseudo boundaries that can train standard temporal action localization detectors without altering their inference. Experiments on four inertial‑sensing benchmarks show that PSEE outperforms adapted point‑supervised baselines, works with various detectors, and remains robust to different point sampling strategies.
By Jiaxi Yin, Ge Wang, Han Ding, Fei Wang