Name
Title | ||
|---|---|---|
Spatio-Temporal Object Detection by Deep Learning: Video Interlacing to Improve Multi-Object Tracking |
Abstract | ||
|---|---|---|
Tracking-by-detection have become a hot topic of great interest to some computer vision applications in the recent years. Generally, the existing tracking-by-detection frameworks have difficulties with congestion, occlusion, and inaccurate detection in crowded scenes. In this paper, we propose a new framework for Multi-Object Tracking-by-Detection (MOT-bD) based on a spatio-temporal interlaced encoding video model and a specialized Deep Convolutional Neural Network (DCNN) detector. The spatio-temporal variation of objects between images are encoded into “interlaced images”. A specialized “interlaced object” convolutional deep detector is trained to detect objects in interlaced images and a classical association algorithm to perform the association between detected objects, since interlaced objects are built to increase overlap during the association step which leads to improve the MOT performance over the same detector/association algorithm applied on non-interlaced images. |
Year | DOI | Venue |
|---|---|---|
2019 | 10.1016/j.imavis.2019.03.002 | Image and Vision Computing |
Keywords | Field | DocType |
Multi-object tracking,Interlacing and inverse interlacing models,Specialization,Interlaced deep detector | Object detection,Computer vision,Interlacing,Pattern recognition,Convolutional neural network,Robustness (computer science),Video tracking,Artificial intelligence,Deep learning,Detector,Mathematics,Encoding (memory) | Journal |
Volume | ISSN | Citations |
88 | 0262-8856 | 1 |
PageRank | References | Authors |
0.35 | 0 | 3 |
Name | Order | Citations | PageRank |
|---|---|---|---|
| Ala Mhalla | 1 | 1 | 0.35 |
| Thierry Chateau | 2 | 149 | 18.92 |
| Najoua Essoukri Ben Amara | 3 | 209 | 41.48 |