Activity Recognition TensorRT

Perform video classification using 3D ResNets trained on Kinetics-400 dataset and accelerated with TensorRT

P.S Click on the gif to watch the full-length video!

Index

• Activity Recogntion TensorRT
• Index
• TensorRT Installation
• Python Dependencies
• Clone the repository
• Download Pretrained Models
• Running the code
• Citations

TensorRT Installation

Assuming you have CUDA already installed, go ahead and download TensorRT from here.

Follow instructions of installing the system binaries and python package for tensorrt here.

Python dependencies

Install the necessary python dependencies by running the following command -

pip3 install -r requirements.txt

Clone the repository

This is a straightforward step, however, if you are new to git recommend glancing threw the steps.

First, install git

sudo apt install git

Next, clone the repository

# Using HTTPS
https://github.com/aj-ames/Activity-Recognition-TensorRT.git
# Using SSH
[email protected]:aj-ames/Activity-Recognition-TensorRT.git

Download Pretrained Models

Download models from google-drive and place them in the current directory.

Running the code

The code supports a number of command line arguments. Use help to see all supported arguments

➜ python3 action_recognition_tensorrt.py --help
usage: action_recognition_tensorrt.py [-h] [--stream STREAM] [--model MODEL] [--fp16] [--frameskip FRAMESKIP]

Object Detection using YOLOv4 and OpenCV4

optional arguments:
  -h, --help            show this help message and exit
  --stream STREAM       Path to use video stream
  --model MODEL         Path to model to use
  --fp16                To enable fp16 precision
  --frameskip FRAMESKIP
                        Number of frames to skip

Run the script this way:

# Video
python3 action_recognition_tensorrt.py --stream /path/to/video --model resnext-101-kinetics.onnx --fp16 --frameskip 3

# Webcam
python3 action_recognition_tensorrt.py --stream webcam --model resnext-101-kinetics.onnx --fp16 --frameskip 3

Citations

@article{hara3dcnns,
  author={Kensho Hara and Hirokatsu Kataoka and Yutaka Satoh},
  title={Can Spatiotemporal 3D CNNs Retrace the History of 2D CNNs and ImageNet?},
  journal={arXiv preprint},
  volume={arXiv:1711.09577},
  year={2017},
}