Papers › Multispectral Deep Neural Networks for Pedestrian Detection
Multispectral Deep Neural Networks for Pedestrian Detection
Jingjing Liu, Shaoting Zhang, Shu Wang, Dimitris N. Metaxas
Multispectral pedestrian detection is essential for around-the-clock applications, e.g., surveillance and autonomous driving. We deeply analyze Faster R-CNN for multispectral pedestrian detection task and then model it into a convolutional network (ConvNet) fusion problem. Further, we discover that ConvNet-based pedestrian detectors trained by color or thermal images separately provide complementary information in discriminating human instances. Thus there is a large potential to improve pedestrian detection by using color and thermal images in DNNs simultaneously. We carefully design four ConvNet fusion architectures that integrate two-branch ConvNets on different DNNs stages, all of which yield better performance compared with the baseline detector. Our experimental results on KAIST pedestrian benchmark show that the Halfway Fusion model that performs fusion on the middle-level convolutional features outperforms the baseline method by 11% and yields a missing rate 3.5% lower than the other proposed architectures.
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Code
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Code Syntology ran Syntology
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Tasks
Results from the paper archive 2025-07-28
| Task | Dataset | Model | Metric | Value | Rank at snapshot | Leaderboard | Report |
|---|---|---|---|---|---|---|---|
| 2D Object Detection | DroneVehicle | HalfwayFusion | Val/mAP50 | 68.2 | #11 of 11 | Archive leaderboard | report |
| Multispectral Object Detection | KAIST Multispectral Pedestrian Detection Benchmark | Halfway Fusion | All Miss Rate | 49.18 | #14 of 17 | Archive leaderboard | report |
| Object Detection | EventPed | Early-Fusion | AP | 47.4 | #5 of 6 | Archive leaderboard | report |
| Object Detection | InOutDoor | Early-Fusion | AP | 58.3 | #6 of 6 | Archive leaderboard | report |
| Object Detection | STCrowd | Early-Fusion | AP | 54.4 | #5 of 6 | Archive leaderboard | report |
Ranks are positions in the archive's leaderboards as they stood at the 2025-07-28 snapshot. Results published since then are not among these rows, so a rank here is not a current standing.
Methods
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