Papers Multi-Object Tracking
“Multi-Object Tracking” 태그가 달린 논문 812편 · 필터 해제
4D Panoptic LiDAR Segmentation
Temporal semantic scene understanding is critical for self-driving cars or robots operating in dynamic environments. In this paper, we propose 4D panoptic LiDAR segmentation to assign a semantic class and a temporally-co…
4D Panoptic SegmentationBenchmarkingMulti-Object TrackingObject Tracking+3RGB-D Railway Platform Monitoring and Scene Understanding for Enhanced Passenger Safety
Automated monitoring and analysis of passenger movement in safety-critical parts of transport infrastructures represent a relevant visual surveillance task. Recent breakthroughs in visual representation learning and spat…
Multi-Object TrackingMultiview DetectionObject DetectionPedestrian Detection+1MOTS R-CNN: Cosine-margin-triplet loss for multi-object tracking
One of the central tasks of multi-object tracking involves learning a distance metric that is consistent with the semantic similarities of objects. The design of an appropriate loss function that encourages discriminativ…
Metric LearningMulti-Object TrackingMulti-Object Tracking and SegmentationObject Tracking+1DEFT: Detection Embeddings for Tracking
Most modern multiple object tracking (MOT) systems follow the tracking-by-detection paradigm, consisting of a detector followed by a method for associating detections into tracks. There is a long history in tracking of c…
3D Multi-Object TrackingMulti-Object TrackingMultiple Object TrackingObject+3Discriminative Appearance Modeling with Multi-track Pooling for Real-time Multi-object Tracking
In multi-object tracking, the tracker maintains in its memory the appearance and motion information for each object in the scene. This memory is utilized for finding matches between tracks and detections and is updated b…
Multi-Object TrackingObjectObject TrackingReal-Time Multi-Object TrackingA two-stage data association approach for 3D Multi-object Tracking
Multi-object tracking (MOT) is an integral part of any autonomous driving pipelines because itproduces trajectories which has been taken by other moving objects in the scene and helps predicttheir future motion. Thanks t…
3D Multi-Object Tracking3D Object DetectionAutonomous DrivingMulti-Object Tracking+51st Place Solution to ECCV-TAO-2020: Detect and Represent Any Object for Tracking
We extend the classical tracking-by-detection paradigm to this tracking-any-object task. Solid detection results are first extracted from TAO dataset. Some state-of-the-art techniques like \textbf{BA}lanced-\textbf{G}rou…
Multi-Object TrackingObjectHarDNet-MSEG: A Simple Encoder-Decoder Polyp Segmentation Neural Network that Achieves over 0.9 Mean Dice and 86 FPS
We propose a new convolution neural network called HarDNet-MSEG for polyp segmentation. It achieves SOTA in both accuracy and inference speed on five popular datasets. For Kvasir-SEG, HarDNet-MSEG delivers 0.904 mean Dic…
DecoderGPUimage-classificationImage Classification+9CityFlow-NL: Tracking and Retrieval of Vehicles at City Scale by Natural Language Descriptions
Natural Language (NL) descriptions can be one of the most convenient or the only way to interact with systems built to understand and detect city scale traffic patterns and vehicle-related events. In this paper, we exten…
Multi-Object TrackingObject TrackingRetrievalTemporal LocalizationHorizontal-to-Vertical Video Conversion
Alongside the prevalence of mobile videos, the general public leans towards consuming vertical videos on hand-held devices. To revitalize the exposure of horizontal contents, we hereby set forth the exploration of automa…
Boundary DetectionMulti-Object TrackingObject TrackingTrackMPNN: A Message Passing Graph Neural Architecture for Multi-Object Tracking
This study follows many classical approaches to multi-object tracking (MOT) that model the problem using dynamic graphical data structures, and adapts this formulation to make it amenable to modern neural networks. Our m…
Autonomous DrivingGraph Neural NetworkMulti-Object TrackingObject+1TrackFormer: Multi-Object Tracking with Transformers
The challenging task of multi-object tracking (MOT) requires simultaneous reasoning about track initialization, identity, and spatio-temporal trajectories. We formulate this task as a frame-to-frame set prediction proble…
DecoderMulti-Object TrackingObjectObject Tracking+1Multi-object Tracking with a Hierarchical Single-branch Network
Recent Multiple Object Tracking (MOT) methods have gradually attempted to integrate object detection and instance re-identification (Re-ID) into a united network to form a one-stage solution. Typically, these methods use…
Multi-Object TrackingMultiple Object TrackingObjectobject-detection+3Weakly Supervised Multi-Object Tracking and Segmentation
We introduce the problem of weakly supervised Multi-Object Tracking and Segmentation, i.e. joint weakly supervised instance segmentation and multi-object tracking, in which we do not provide any kind of mask annotation. …
Instance SegmentationMulti-Object TrackingMulti-Object Tracking and SegmentationMulti-Task Learning+6Beyond the Pixels: Exploring the Effects of Bit-Level Network and File Corruptions on Video Model Robustness
We investigate the robustness of video machine learning models to bit-level network and file corruptions, which can arise from network transmission failures or hardware errors, and explore defenses against such corruptio…
Action RecognitionMulti-Object TrackingObject TrackingA General Recurrent Tracking Framework Without Real Data
Recent progress in multi-object tracking (MOT) has shown great significance of a robust scoring mechanism for potential tracks. However, the lack of available data in MOT makes it difficult to learn a general scoring…
Multi-Object TrackingObject TrackingContinuous Copy-Paste for One-Stage Multi-Object Tracking and Segmentation
Current one-step multi-object tracking and segmentation (MOTS) methods lag behind recent two-step methods. By separating the instance segmentation stage from the tracking stage, two-step methods can exploit non-video…
Data AugmentationInstance SegmentationMulti-Object TrackingMulti-Object Tracking and Segmentation+3Assignment-Space-Based Multi-Object Tracking and Segmentation
Multi-object tracking and segmentation (MOTS) is important for understanding dynamic scenes in video data. Existing methods perform well on multi-object detection and segmentation for independent video frames, but tr…
Multi-Object TrackingMulti-Object Tracking and SegmentationObjectobject-detection+3TransTrack: Multiple Object Tracking with Transformer
In this work, we propose TransTrack, a simple but efficient scheme to solve the multiple object tracking problems. TransTrack leverages the transformer architecture, which is an attention-based query-key mechanism. It ap…
Multi-Object TrackingMultiple Object TrackingMultiple Object Tracking with TransformerObject+3Probabilistic 3D Multi-Modal, Multi-Object Tracking for Autonomous Driving
Multi-object tracking is an important ability for an autonomous vehicle to safely navigate a traffic scene. Current state-of-the-art follows the tracking-by-detection paradigm where existing tracks are associated with de…
3D Pedestrian TrackingAutonomous DrivingManagementMulti-Object Tracking+5