Vehicle_Detection_Recognition-master
所属分类：模式识别(视觉/语音等)
开发工具：matlab
文件大小：76KB
下载次数：2
上传日期：2020-04-08 01:02:25
上 传 者：
有饭吃
说明： 拥有GUI界面，支持二次训练二次开发的车辆识别程序
(Have GUI interface, support the vehicle identification program of secondary training secondary development)
文件列表:
CarProphet.m (3201, 2017-03-30)
core_func (0, 2017-03-30)
core_func\draw.m (391, 2017-03-30)
core_func\draw_roi_class.m (358, 2017-03-30)
core_func\get_highscore_proposals.m (277, 2017-03-30)
GUI (0, 2017-03-30)
GUI\MainWindow.fig (70136, 2017-03-30)
GUI\MainWindow.m (8785, 2017-03-30)
Model (0, 2017-03-30)
Model\Download.txt (250, 2017-03-30)
Old (0, 2017-03-30)
Old\Pic_Detect_Recognition.m (765, 2017-03-30)
Old\Start.m (302, 2017-03-30)
Old\Video_Detect_Recognition.m (960, 2017-03-30)
start.m (187, 2017-03-30)
新建文本文档.txt (0, 2020-04-08)
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## UPDATE!!!
We redesigned the GUI, and the program was optimized.
Have a look:
1、4 steps for pic recognition:
2、You can select bounding-box by yourself:
3、Video:
4、Load your own model:
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# Vehicle_Detection_Recognition
This is a Matlab lesson design for vehicle detection and recognition. Using cifar-10Net to training a RCNN, and finetune AlexNet to classify. Thanks to Cars Dataset : http://ai.stanford.edu/~jkrause/cars/car_dataset.html
## Software
Matlab R2016b
## Doadload
cars_meta.mat : http://pan.baidu.com/s/1mi6nvr6
cifar10NetRCNN.mat : (for Car position detection) http://pan.baidu.com/s/1geLa1V1
AlexNet_New.mat : (for Car type classify) http://pan.baidu.com/s/1bEzcYE
## Code
You can use it to finish your task for single picture or video. Make sure your picture or video frame has 3 channels (colorful)
## Citation
3D Object Representations for Fine-Grained Categorization
Jonathan Krause, Michael Stark, Jia Deng, Li Fei-Fei
4th IEEE Workshop on 3D Representation and Recognition, at ICCV 2013 (3dRR-13). Sydney, Australia. Dec. 8, 2013.
DataSet : http://ai.stanford.edu/~jkrause/cars/car_dataset.html
## Effect
Actually, The running speed of the program is a bit of slow... Hope you can try Faster-Rcnn or yolo (you only look once).
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