LRCN_activity_recognition

最新推荐文章于 2025-03-06 10:31:10 发布

BojackHorseman

最新推荐文章于 2025-03-06 10:31:10 发布

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分类专栏：论文阅读 caffe python

本文链接：https://blog.youkuaiyun.com/bojackhosreman/article/details/71757474

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这篇博客详细介绍了如何使用Caffe框架重新训练LRCN（Long Short-Term Memory）活动识别模型。步骤包括从UCF-101视频中提取RGB帧和计算光流，然后训练单帧模型和LRCN模型。提供的脚本包括run_singleFrame_RGB.sh、run_singleFrame_flow.sh、run_lstm_RGB.sh和run_lstm_flow.sh。

摘要生成于 C知道，由 DeepSeek-R1 满血版支持，前往体验 >

http://www.eecs.berkeley.edu/~lisa_anne/LRCN_video

作者用的是caffe。。

Steps to retrain the LRCN activity recognition models:

Extract RGB frames: The script “extract_frames.sh” will convert UCF-101 .avi files to .jpg images. I extracted frames at 30 frames/second.
提取视频帧。
Compute flow frames: After downloading the code from [1], you can use “create_flow_images_LRCN.m” to compute flow frames. Example flow images for the video “YoYo_g25_c03” are here.
计算光流。
Train single frame models: Finetune the hybrid model (found here) with video frames to train a single frame model. Use “run_singleFrame_RGB.sh” and “run_singleFrame_flow.sh” to train the RGB and flow models respectively. Make sure to change the “root_folder” param in “train_test_singleFrame_RGB.prototxt” and “train_test_singleFrame_flow.prototxt” as needed. The single frame models I trained can be found here.
对RGB和flow分别训练baseline模型。
Train LRCN models: Using the single frame models as a starting point, train the LRCN models by running “run_lstm_RGB.sh” and “run_lstm_flow.sh“. The data layer for the LRCN model is a python layer (“sequence_input_layer.py”). Make sure to set “WITH_PYTHON_LAYER := 1” in Makefile.config. Change the paths “flow_frames” and “RGB_frames” in “sequence_input_layer.py” as needed. The models I trained can be found here.
分别训练LRCN模型。

run_singleFrame_RGB.sh

#!/bin/sh
TOOLS=../../build/tools

GLOG_logtostderr=1 $TOOLS/caffe train -solver singleFrame_solver_RGB.prototxt -weights caffe_imagenet_hyb2_wr_rc_solver_sqrt_iter_310000 
echo 'Done.'

GLOG_logtostderr=1 设置glog日志。glog是google 出的一个C++轻量级日志库，介绍请看