SymTrack is the first systematic detection-free framework for scene text tracking that constructs benchmarks from video text spotting datasets and reports up to 11.97% AUC gains over prior trackers.
(1997).Public transport and cycling: Experience of modal integration in ger-747 many
5 Pith papers cite this work. Polarity classification is still indexing.
5
Pith papers citing it
fields
cs.CV 5verdicts
UNVERDICTED 5representative citing papers
FATE combines pillar encoding via orthogonal polynomial basis with frequency-aware training to enable event-based object detection at up to 200 Hz without internal temporal sub-binning.
Tetris decomposes stationary videos into tile polyominoes and applies classifier plus ILP pruning to cut detector calls, staying within 5% accuracy loss while delivering up to 17.4x throughput gains over priors.
Applying multi-object tracking to fuse softmax probabilities across frames in camera trap data yields weighted F1-score gains of 5.1%, 3.1%, and 2.0% over standalone classifiers on three datasets.
A framework segments panoramic video into sub-images for detection, modifies multi-object tracking for boundary continuity, and applies it to vehicle overtaking detection in real cycling videos, reporting gains in precision and an F-score of 0.82.
citing papers explorer
-
Beyond Detection: A Structure-Aware Framework for Scene Text Tracking
SymTrack is the first systematic detection-free framework for scene text tracking that constructs benchmarks from video text spotting datasets and reports up to 11.97% AUC gains over prior trackers.
-
FATE: Pillar Encoding and Frequency-Aware Training for Event-Based Object Detection
FATE combines pillar encoding via orthogonal polynomial basis with frequency-aware training to enable event-based object detection at up to 200 Hz without internal temporal sub-binning.
-
Tetris: Tile-level Sampling for Efficient and High-Fidelity Video Object Tracking
Tetris decomposes stationary videos into tile polyominoes and applies classifier plus ILP pruning to cut detector calls, staying within 5% accuracy loss while delivering up to 17.4x throughput gains over priors.
-
Multi-Object Tracking Consistently Improves Wildlife Inference
Applying multi-object tracking to fuse softmax probabilities across frames in camera trap data yields weighted F1-score gains of 5.1%, 3.1%, and 2.0% over standalone classifiers on three datasets.
-
Multiple Object Detection and Tracking in Panoramic Videos for Cycling Safety Analysis
A framework segments panoramic video into sub-images for detection, modifies multi-object tracking for boundary continuity, and applies it to vehicle overtaking detection in real cycling videos, reporting gains in precision and an F-score of 0.82.