文件列表:

voc-release1\clipboxes.m (296, 2008-05-10)
voc-release1\compile.m (52, 2008-05-10)
voc-release1\COPYING (1079, 2008-05-10)
voc-release1\demo.m (812, 2008-05-10)
voc-release1\detect.m (6175, 2008-05-10)
voc-release1\dt.cc (2511, 2008-05-10)
voc-release1\fconv.cc (2558, 2008-05-10)
voc-release1\featpyramid.m (1166, 2008-05-10)
voc-release1\features.cc (5683, 2008-05-13)
voc-release1\foldHOG.m (138, 2008-05-10)
voc-release1\HOGpicture.m (622, 2008-05-10)
voc-release1\image1.jpg (35232, 2008-05-10)
voc-release1\image2.jpg (17311, 2008-05-10)
voc-release1\models (0, 2017-09-15)
voc-release1\models\bicycle_model.mat (91968, 2008-05-10)
voc-release1\models\bicycle_pr.jpg (37347, 2008-05-10)
voc-release1\models\bus_model.mat (99180, 2008-05-10)
voc-release1\models\bus_pr.jpg (36700, 2008-05-10)
voc-release1\models\car_model.mat (104695, 2008-05-10)
voc-release1\models\car_pr.jpg (35979, 2008-05-10)
voc-release1\models\cat_model.mat (104995, 2008-05-10)
voc-release1\models\cat_pr.jpg (37986, 2008-05-10)
voc-release1\models\cow_model.mat (91962, 2008-05-10)
voc-release1\models\cow_pr.jpg (36807, 2008-05-10)
voc-release1\models\dog_model.mat (92508, 2008-05-10)
voc-release1\models\dog_pr.jpg (37483, 2008-05-10)
voc-release1\models\horse_model.mat (92508, 2008-05-10)
voc-release1\models\horse_pr.jpg (37150, 2008-05-10)
voc-release1\models\motorbike_model.mat (95293, 2008-05-10)
voc-release1\models\motorbike_pr.jpg (38027, 2008-05-10)
voc-release1\models\person_model.mat (45421, 2008-05-10)
voc-release1\models\person_pr.jpg (37364, 2008-05-10)
voc-release1\models\scores (173, 2008-05-10)
voc-release1\models\sheep_model.mat (78135, 2008-05-10)
voc-release1\models\sheep_pr.jpg (37107, 2008-05-10)
voc-release1\nms.m (997, 2008-05-10)
voc-release1\resize.cc (3250, 2008-05-10)
voc-release1\showboxes.m (487, 2008-05-10)
voc-release1\visualizeHOG.m (491, 2008-05-10)
... ...

Information =========== This is an implementation of the system described in: P. Felzenszwalb, D. McAllester, D. Ramaman. A Discriminatively Trained, Multiscale, Deformable Part Model. To appear in CVPR 2008. We are currently distributing the object detection code with models trained on the PASCAL VOC 2006 dataset. The learning code will be made available in the future. Please note this implementation is not identical to the original one (as described in the paper). In particular the part displacements are not constrained to be in a bounded range. This leads to a slight improvement in performance for some object categories. Basic Usage =========== The system is implemented in matlab, with a few helper functions written in C++ for efficiency reasons. Basic steps for running the system: 1. Unpack the code to a directory. 2. Start matlab from the directory with the code. 3. Run the 'compile' script. This will compile the C++ helper functions. 4. Run the 'demo' script. This will run the object detector on a test image. The main functions defined by the code are: boxes = detect(im, model, thresh) top = nms(boxes, overlap) boxes = clipboxes(im, boxes) showboxes(im, boxes) visualizemodel(model) Their usage is demonstrated in the 'demo' script. The directory 'models' contains matlab datafiles with models trained on the PASCAL VOC 2006 train+val dataset. The score of each model in the test dataset is recorded in the file 'scores'. Loading a model datafile into matlab will define a variable 'model' with the model trained for a particular object category. The value 'model.thresh' defines a threshold that can be used in the 'detect' function to obtain a high recall rate (at the expense of many false positives per image).

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