Video Object Detection

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3 papers in the last four weeks, against 1 the four weeks before. 0.0% of all new papers.

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Analytical and Convolutional Neural Network-Based Motion-Vector Propagation for Efficient Video Object Detection
Sep 28, 2026Ashiyana Abdul Majeed, Mahmoud Meribout, Neethu JosephEfficient Neural Network Inference Edge Computing
Department of Computer and Information Engineering, Khalifa University, Abu Dhabi, UAE
mbariml: a curation pipeline for turning deep-sea imagery and video into object-detection training data
Sep 21, 2026Lonny Lundsten, Kevin Barnard, Dave CaressTraining Data Curation Human-in-the-Loop Annotation
Monterey Bay Aquarium Research Institute
VANTAGE-Bench: Evaluating the Infrastructure AI Gap in Vision-Language Models
Sep 10, 2026Zaid Pervaiz Bhat, Nimra Nayyar, Arihant Jain +6VLM Evaluation Vision-Language Models
NVIDIA · Clemson University
MVTrack: Ultrafast Appearance-Free Moving Object Tracking from Compressed Bitstreams
Aug 11, 2026Iñaki Erregue, Kamal Nasrollahi, Sergio EscaleraVisual Object Tracking Video Object Detection
Universitat de Barcelona · Computer Vision Center · Milestone Systems +1
Depth-Guided Video Object Counting in Crowded Scenes
Aug 6, 2026Yuanjing Xu, Xinyan Liu, Weidong Chen +5Video Object Detection Object Detection
Harbin Institute of Technology (Weihai) Weihai, Shandong, China · City University of Hong Kong Hong Kong, China · University of Science and Technology of China Hefei, Anhui, China +1
Small-Pollinator Detection in Cluttered Field Video
Jul 24, 2026Onur Onal, Chen ChenSmall Object Detection Detection Transformer
Iowa State University · Iowa State University, Ames, IA, USA · Institute of AI, University of Central Florida +1
Dual-Correlation Hypergraph Network for Unaligned RGBT Video Object Detection and A Large-scale Benchmark
Jul 9, 2026Qishun Wang, Yapeng Li, Zhengzheng Tu +2Cross-Modal Alignment Video Object Detection
Anhui Provincial Key Laboratory of Multimodal Cognitive Computation, School of Computer Science and Technology, Anhui University, Hefei 230601, China
Computer Vision for Wildlife Monitoring: Detecting Brown Howler Monkeys using YOLO
Jul 1, 2026Gabriel Ferri Schneider, Guido Luis Glufke Mainardi, Paulo Ricardo Knob +4Video Object Detection Object Detection
PUCRS · UERGS · SEMA
Temporal Preservation over Processing: Diagnosing and Designing Spatiotemporal Single-Stage Video Detectors
Jun 30, 2026Karam Tomotaki-Dawoud, Anna Hilsmann, Peter Eisert +1Video Object Detection Real-Time Object Detection
Fraunhofer HHI, Berlin, Germany
Learn Temporal Consistency For Robust Satellite Video Detector
Jun 13, 2026Weilong Guo, Shengyang Li, Yanfeng GuVideo Object Detection Object Detection
Technology and Engineering Center for Space Utilization, Chinese Academy of Sciences, Beijing 100094, China · Key Laboratory of Space Utilization, Chinese Academy of Sciences, Beijing 100094, China · University of Chinese Academy of Sciences, Beijing 100049, China +1
A Geometry-Informed Computer Vision Method for Detecting and Examining Overtaking Vehicles From A Bicycle
Jun 3, 2026Gandhimathi Padmanaban, Rayane Moustafa, Fred FengVideo Object Detection
Department of Industrial Manufacturing and Systems Engineering, University of Michigan-Dearborn, Michigan, USA, 48128
FindIt: A Format-Informed Visual Detection Benchmark for Generalist Multimodal LLMs
Jun 2, 2026Eshika Khandelwal, Jingjing Pan, Mingfang Zhang +3Multimodal Grounding Video Object Detection
Tuebingen AI Center, University of Tuebingen · Woven by Toyota, Inc., Tokyo, Japan · Toyota Motor Europe, Brussels, Belgium
SteelDS: A High-Resolution Video Dataset of E40 Steel Scrap for Object Detection and Instance Segmentation
May 26, 2026Melanie Neubauer, Christian Rauch, Gerald Koinig +3Instance Segmentation Video Object Detection
Chair of Cyber-Physical-Systems, Technical University of Leoben, Franz Josef-Straße 18, Leoben, 8700, Styria, Austria. · Chair of Waste Processing Technology and Waste Management, Technical University of Leoben, Franz Josef-Straße 18, Leoben, 8700, Styria, Austria.
Decoupling Ego-Motion from Target Dynamics via Dual-Interval Motion Cues for UAV Detection
May 21, 2026Liuyang Wang, Feitian ZhangVideo Object Detection Object Detection
Robotics and Control Laboratory, Department of Robotics, School of Advanced Manufacturing and Robotics, and the State Key Laboratory of Turbulence and Complex Systems, Peking University, Beijing, 100871, China · Great Bay University, Guangdong 523808, China
MR2-ByteTrack: CNN and Transformer-based Video Object Detection for AI-augmented Embedded Vision Sensor Nodes
May 14, 2026Luca Bompani, Manuele Rusci, Luca Benini +2Edge Inference Efficient Object Detection
Electrical, Electronic and Information Engineering (DEI), University of Bologna, Italy. · Department of Electrical Engineering (ESAT), KU Leuven, Belgium. · Integrated Systems Laboratory (IIS), ETH Zürich, Switzerland. +1
BronchoLumen: Analysis of recent YOLO-based architectures for real-time bronchial orifice detection in video bronchoscopy
May 12, 2026Yongchao Li, Marian HimstedtMedical Imaging Medical Image Analysis
Faculty of Electrical Engineering and Computer Science, Technical University of Applied Sciences Lübeck, Mönkhofer Weg 239, Lübeck, 23562, Germany.
VVitCutLER: Towards Unsupervised Object Detection and Segmentation in Videos
May 11, 2026Zhijing Lu, Khurram Azeem Hashmi, Didier Stricker +1Video Object Segmentation Instance Segmentation
RPTU University of Kaiserslautern-Landau, Germany · German Research Center for Artificial Intelligence, Germany
Closed-Circuit Television Data as an Emergent Data Source for Urban Rail Platform Crowding Estimation
Aug 3, 2025Riccardo Fiorista, Awad Abdelhalim, Anson F. Stewart +3Intelligent Transportation Systems Crowd Counting
Transit Lab Department of Urban Studies and Planning Massachusetts Institute of Technology, Cambridge, MA 02139, USA · Washington Metropolitan Area Transit Authority 300 7th Street SW, Washington, DC 20024, USA
XS-VID: A Large-Scale Benchmark for Small Object Detection and Tracking in Videos
Jul 25, 2024Jiahao Guo, Ziyang Xu, Lianjun Wu +3Small Object Detection Video Object Detection
Huazhong University of Science and Technology
Representation Recycling for Streaming Video Analysis
Apr 28, 2022Can Ufuk Ertenli, Ramazan Gokberk Cinbis, Emre AkbasStreaming Video Understanding Video Object Detection
Department of Computer Engineering, Middle East Technical University (METU), Ankara, Turkey · Center for Robotics and Artificial Intelligence (ROMER), Middle East Technical University (METU), Ankara, Turkey

Sep 28, 2026, cs.CVJ/K move · Enter open · S save

Analytical and Convolutional Neural Network-Based Motion-Vector Propagation for Efficient Video Object Detection

Ashiyana Abdul Majeed, Mahmoud Meribout, Neethu Joseph

Department of Computer and Information Engineering, Khalifa University, Abu Dhabi, UAE

Efficient Neural Network Inference Edge Computing Video Object Detection Convolutional Neural Networks

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Continuous video analytics requires accurate localization at low latency within embedded power budgets. This paper presents a hardware-software design methodology that reuses codec motion vectors (MVs) between detector invocations. Two alternative models support translation and scale changes: analytical motion-vector propagation (Analytical-MV) and learned propagation using a convolutional neural network (CNN) (CNN-MV). The learned model uses convolutional operations and independent object updates suited to parallel execution on an edge graphics processing unit (GPU). Analytical-MV combines a harmonic-mean precision-recall score (F1) of 0.909 with a mean end-to-end latency of 9.03 ms and an energy consumption of 0.177 J per frame, yielding the lowest latency and energy among the evaluated configurations. Relative to detection on every frame, it reduces mean latency by 25.9% and energy per frame by 36.4%. CNN-MV offers a different trade-off: its fastest configuration raises recall from 0.871 for Analytical-MV to 0.890 and lowers mean power from 19.64 to 17.32 W, while achieving a latency of 18.42 ms and an energy consumption of 0.319 J per frame. It is therefore useful when recall or operating power is more important than minimum latency and energy. Execution on a deep learning accelerator (DLA) further reduces time-averaged GPU utilization relative to GPU execution. Host-processing optimization substantially improves both latency and energy, demonstrating the value of jointly designing temporal models and their execution pipelines.

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