Recognizing Ultra-High-Speed Moving Objects with Bio-Inspired Spike Camera
Authors: Junwei Zhao, Shiliang Zhang, Zhaofei Yu, Tiejun Huang
AAAI 2024 | Conference PDF | Archive PDF | Plain Text | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Experimental | Experimental results show that the proposed method achieves 73.2% accuracy in recognizing 10 classes of ultra-high-speed moving objects, outperforming existing spike-based recognition methods. |
| Researcher Affiliation | Academia | Junwei Zhao1,2, Shiliang Zhang1 , Zhaofei Yu1,2 , Tiejun Huang1,2 1National Key Laboratory for Multimedia Information Processing, School of Computer Science, Peking University 2Institute for Artificial Intelligence, Peking University {jwz, slzhang.jdl, yuzf12, tjhuang}@pku.edu.cn |
| Pseudocode | No | The paper includes figures illustrating the proposed architecture and modules, but it does not contain any formal pseudocode or algorithm blocks. |
| Open Source Code | Yes | Resources will be available at https://github.com/Evin-X/UHSR. |
| Open Datasets | Yes | Additionally, this paper contributes an original real-captured spiking recognition dataset consisting of 12,000 ultra-high-speed (equivalent speed > 500 km/h) moving objects. ... Resources will be available at https://github.com/Evin-X/UHSR. ... Besides above methods, this paper contributes a spiking dataset for Ultra-High-Speed object Recognition, named UHSR dataset. |
| Dataset Splits | No | Training and testing sets are spilt as a ratio of 5:1. The paper mentions training and testing splits but does not explicitly mention a separate validation set split. |
| Hardware Specification | Yes | The framework is implemented in Py Torch and trained on NVIDIA RTX 4090 GPUs. |
| Software Dependencies | No | The paper mentions "Py Torch" as the implementation framework but does not specify a version number for PyTorch or any other software dependencies with their versions. |
| Experiment Setup | Yes | We adopt the Image Net pre-trained Res Net-18 as the backbone. Model parameters are trained using the SGD optimizer without data augmentation. The initial learning rate is set to 2e-4 with the Lambda LR scheduler. The training process runs for 30 epochs for each dataset with a batch size of 16. Input samples are downsampled to 124 124 using average pooling. θ/λ is set to 1, constraining the value of brightness intensity maps within [0, 1]. |