文件列表:

code1\alldist.m (268, 2004-05-11)
code1\anchors.m (1227, 2004-05-25)
code1\calcdist.m (682, 2004-05-21)
code1\data\actioncliptrain00182.avi (2435072, 2009-04-07)
code1\data\hollywood2_samples\actioncliptest00002.txt (73819, 2010-06-08)
code1\data\hollywood2_samples\actioncliptest00008.txt (54349, 2010-06-08)
code1\data\hollywood2_samples\actioncliptest00015.txt (367164, 2010-06-08)
code1\data\hollywood2_samples\actioncliptest00022.txt (208726, 2010-06-08)
code1\data\hollywood2_samples\actioncliptest00025.txt (208983, 2010-06-08)
code1\data\hollywood2_samples\actioncliptest00026.txt (73941, 2010-06-08)
code1\data\hollywood2_samples\actioncliptest00027.txt (109199, 2010-06-08)
code1\data\hollywood2_samples\actioncliptest00028.txt (29075, 2010-06-08)
code1\data\hollywood2_samples\actioncliptest00030.txt (98599, 2010-06-08)
code1\data\hollywood2_samples\actioncliptest00045.txt (86252, 2010-06-08)
code1\data\hollywood2_samples\actioncliptest00047.txt (96746, 2010-06-08)
code1\data\hollywood2_samples\actioncliptest00049.txt (39256, 2010-06-08)
code1\data\hollywood2_samples\actioncliptest00050.txt (120726, 2010-06-08)
code1\data\hollywood2_samples\actioncliptest00052.txt (76546, 2010-06-08)
code1\data\hollywood2_samples\actioncliptest00059.txt (292199, 2010-06-08)
code1\data\hollywood2_samples\actioncliptest00060.txt (240491, 2010-06-08)
code1\data\hollywood2_samples\actioncliptest00067.txt (51053, 2010-06-08)
code1\data\hollywood2_samples\actioncliptest00069.txt (271166, 2010-06-08)
code1\data\hollywood2_samples\actioncliptest00071.txt (93514, 2010-06-08)
code1\data\hollywood2_samples\actioncliptest00076.txt (199424, 2010-06-08)
code1\data\hollywood2_samples\actioncliptest00077.txt (101165, 2010-06-08)
code1\data\hollywood2_samples\actioncliptest00087.txt (31209, 2010-06-08)
code1\data\hollywood2_samples\actioncliptest00094.txt (294631, 2010-06-08)
code1\data\hollywood2_samples\actioncliptest00102.txt (56928, 2010-06-08)
code1\data\hollywood2_samples\actioncliptest00104.txt (238877, 2010-06-08)
code1\data\hollywood2_samples\actioncliptest00106.txt (82545, 2010-06-08)
code1\data\hollywood2_samples\actioncliptest00109.txt (178935, 2010-06-08)
code1\data\hollywood2_samples\actioncliptest00110.txt (261792, 2010-06-08)
code1\data\hollywood2_samples\actioncliptest00111.txt (33574, 2010-06-08)
code1\data\hollywood2_samples\actioncliptest00112.txt (135503, 2010-06-08)
code1\data\hollywood2_samples\actioncliptest00121.txt (57852, 2010-06-08)
code1\data\hollywood2_samples\actioncliptest00125.txt (74317, 2010-06-08)
code1\data\hollywood2_samples\actioncliptest00131.txt (79322, 2010-06-08)
code1\data\hollywood2_samples\actioncliptest00149.txt (154256, 2010-06-08)
code1\data\hollywood2_samples\actioncliptest00150.txt (276392, 2010-06-08)
code1\data\hollywood2_samples\actioncliptest00182.txt (102419, 2010-06-08)
... ...

--- STIP implementation v1.0 --- (18-06-2008) http://www.irisa.fr/vista/Equipe/People/Laptev/download/stip-1.0.zip Authors: ======== This code was developed in 2006-2008 jointly at INRIA Rennes (http://www.irisa.fr/vista) and IDIAP (www.idiap.ch) under supervision of Ivan Laptev and Barbara Caputo. The code is provided as-it-is without any warranty. For questions and bug reports contact Ivan Laptev (ivan.laptev@inria.fr) General: ======== The code in this directory detects Space-Time Interest Points (STIPs) and computes corresponding local space-time descriptors. The currently implemented detector resembles the extended space-time Harris detector described in [Laptev IJCV'05]. The code does not implement scale selection but detects points for a set of multiple combinations of spatial and temporal scales. This simplification appears to produce similar (or better) results in applications (e.g. action recognition) while resulting in a considerable speed-up and close-to-video-rate run time. The currently implemented types of descriptors are HOG (Histograms of Oriented Gradients) and HOF (Histograms of Optical Flow) computed on a 3D video patch in the neighborhood of each detected STIP. The patch is partitioned into a grid with 3x3x2 spatio-temporal blocks; 4-bin HOG descriptors and 5-bin HOF descriptors are then computed for all blocks and are concatenated into a 72-element and 90-element descriptors respectively. Input/Output: ============= The input can be either a video file (supported either by Windows video codecs or ffmpeg library under Linux) or a video stream from the camera supported by OpenCV camera interface. For a video input the frame interval for processing can be specified. The position and descriptors of detected STIPs will be saved in a text format. Run "./bin/stipdet --help" to get further details. Dependencies: ============= OpenCV library (http://www.intel.com/technology/computing/opencv/) On Linux OpenCV must be compiled with ffmpeg support, see e.g.: http://www.comp.leeds.ac.uk/vision/opencv/install-lin-ffmpeg.html Running ======= There are two executables in ./bin directory ./bin/stipdet : detection and saving of STIP features ./bin/stipshow : visualization of STIP points with optional video dump Run "./bin/stipdet --help" and "./bin/stipshow --help" to learn about the format of command line I/O parameters. Evaluation: =========== This STIP version has been tested on KTH action recognition benchmark (http://www.nada.kth.se/cvap/actions). The results are comparable to the ones reported in [Laptev et al. CVPR 2008] and to the date (June 2008) correspond to the best known performance on KTH dataset. We evaluated the features using single channel Bag-of-Features SVM classification framework as in [Laptev et al. CVPR 2008]. Average accuracy for action recognition on KTH dataset using different descriptors in this package are as follows: hog: 80.5% hof: 90.6% hoghof: 91.3% (hoghof is a concatenation of hog and hof descriptors) Example 1 (detection): ====================== >./bin/stipdet -f ./data/walk-simple.avi -o ./data/walk-simple-stip.txt Options summary: video input: data/walk-simple.avi frame interval: 0-100000000 output file: data/walk-simple-stip.txt #pyr.levels: 3 init.pyr.level: 0 patch size fct.: 5 descriptor type: hoghof Fame: 20 - IPs[this: 0, total: 0] - Perf: Avg FPS=9.7 Fame: 40 - IPs[this: 6, total: 63] - Perf: Avg FPS=9.6 Fame: 60 - IPs[this: 5, total: 110] - Perf: Avg FPS=9.6 Fame: 80 - IPs[this: 1, total: 161] - Perf: Avg FPS=9.6 Fame: 100 - IPs[this: 0, total: 203] - Perf: Avg FPS=9.6 -> detected 242 points Example 2 (visualization): ========================== >./bin/stipshow -v ./data/walk-simple.avi -f ./data/walk-simple-stip.txt -o ./data/walk-simple-stip.avi Input video: data/walk-simple.avi Input features:data/walk-simple-stip.txt load 243 features from data/walk-simple-stip.txt Output #0, avi, to 'data/walk-simple-stip.avi': Stream #0.0: Video: mpeg4, 160x120, 25.00 fps, q=2-31, 800 kb/s Links ===== Action recognition page and related papers using STIP features: http://www.irisa.fr/vista/actions

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