--- annotations_creators: - expert-generated language_creators: - expert-generated language: - en license: - other multilinguality: - monolingual pretty_name: Biwi Kinect Head Pose Database size_categories: - 10K View C++ Code ```cpp /* * Gabriele Fanelli * * fanelli@vision.ee.ethz.ch * * BIWI, ETHZ, 2011 * * Part of the Biwi Kinect Head Pose Database * * Example code for reading a compressed binary depth image file. * * THE SOFTWARE IS PROVIDED “AS IS” AND THE PROVIDER GIVES NO EXPRESS OR IMPLIED WARRANTIES OF ANY KIND, * INCLUDING WITHOUT LIMITATION THE WARRANTIES OF FITNESS FOR ANY PARTICULAR PURPOSE AND NON-INFRINGEMENT. * IN NO EVENT SHALL THE PROVIDER BE HELD RESPONSIBLE FOR LOSS OR DAMAGE CAUSED BY THE USE OF THE SOFTWARE. * * */ #include #include #include int16_t* loadDepthImageCompressed( const char* fname ){ //now read the depth image FILE* pFile = fopen(fname, "rb"); if(!pFile){ std::cerr << "could not open file " << fname << std::endl; return NULL; } int im_width = 0; int im_height = 0; bool success = true; success &= ( fread(&im_width,sizeof(int),1,pFile) == 1 ); // read width of depthmap success &= ( fread(&im_height,sizeof(int),1,pFile) == 1 ); // read height of depthmap int16_t* depth_img = new int16_t[im_width*im_height]; int numempty; int numfull; int p = 0; while(p < im_width*im_height ){ success &= ( fread( &numempty,sizeof(int),1,pFile) == 1 ); for(int i = 0; i < numempty; i++) depth_img[ p + i ] = 0; success &= ( fread( &numfull,sizeof(int), 1, pFile) == 1 ); success &= ( fread( &depth_img[ p + numempty ], sizeof(int16_t), numfull, pFile) == (unsigned int) numfull ); p += numempty+numfull; } fclose(pFile); if(success) return depth_img; else{ delete [] depth_img; return NULL; } } float* read_gt(const char* fname){ //try to read in the ground truth from a binary file FILE* pFile = fopen(fname, "rb"); if(!pFile){ std::cerr << "could not open file " << fname << std::endl; return NULL; } float* data = new float[6]; bool success = true; success &= ( fread( &data[0], sizeof(float), 6, pFile) == 6 ); fclose(pFile); if(success) return data; else{ delete [] data; return NULL; } } ``` ### Supported Tasks and Leaderboards Biwi Kinect Head Pose Database supports the following tasks : - Head pose estimation - Pose estimation - Face verification ### Languages [Needs More Information] ## Dataset Structure ### Data Instances A sample from the Biwi Kinect Head Pose dataset is provided below: ``` { 'sequence_number': '12', 'subject_id': 'M06', 'rgb': [,.....], 'rgb_cal': { 'intrisic_mat': [[517.679, 0.0, 320.0], [0.0, 517.679, 240.5], [0.0, 0.0, 1.0]], 'extrinsic_mat': { 'rotation': [[0.999947, 0.00432361, 0.00929419], [-0.00446314, 0.999877, 0.0150443], [-0.009228, -0.015085, 0.999844]], 'translation': [-24.0198, 5.8896, -13.2308] } } 'depth': ['../hpdb/12/frame_00003_depth.bin', .....], 'depth_cal': { 'intrisic_mat': [[575.816, 0.0, 320.0], [0.0, 575.816, 240.0], [0.0, 0.0, 1.0]], 'extrinsic_mat': { 'rotation': [[1.0, 0.0, 0.0], [0.0, 1.0, 0.0], [0.0, 0.0, 1.0]], 'translation': [0.0, 0.0, 0.0] } } 'head_pose_gt': { 'center': [[43.4019, -30.7038, 906.864], [43.0202, -30.8683, 906.94], [43.0255, -30.5611, 906.659], .....], 'rotation': [[[0.980639, 0.109899, 0.162077], [-0.11023, 0.993882, -0.00697376], [-0.161851, -0.011027, 0.986754]], ......] } } ``` ### Data Fields - `sequence_number` : This refers to the sequence number in the dataset. There are a total of 24 sequences. - `subject_id` : This refers to the subjects in the dataset. There are a total of 20 people with 6 females and 14 males where 4 people were recorded twice. - `rgb` : List of png frames containing the poses. - `rgb_cal`: Contains calibration information for the color camera which includes intrinsic matrix, global rotation and translation. - `depth` : List of depth frames for the poses. - `depth_cal`: Contains calibration information for the depth camera which includes intrinsic matrix, global rotation and translation. - `head_pose_gt` : Contains ground truth information, i.e., the location of the center of the head in 3D and the head rotation, encoded as a 3x3 rotation matrix. ### Data Splits All the data is contained in the training set. ## Dataset Creation ### Curation Rationale [More Information Needed] ### Source Data #### Initial Data Collection and Normalization The Biwi Kinect Head Pose Database is acquired with the Microsoft Kinect sensor, a structured IR light device. #### Who are the source language producers? [More Information Needed] ### Annotations #### Annotation process From Dataset's README : > The database contains 24 sequences acquired with a Kinect sensor. 20 people (some were recorded twice - 6 women and 14 men) were recorded while turning their heads, sitting in front of the sensor, at roughly one meter of distance. #### Who are the annotators? [More Information Needed] ### Personal and Sensitive Information [More Information Needed] ## Considerations for Using the Data ### Social Impact of Dataset [More Information Needed] ### Discussion of Biases [More Information Needed] ### Other Known Limitations [More Information Needed] ## Additional Information ### Dataset Curators [Needs More Information] ### Licensing Information From Dataset's README : > This database is made available for non-commercial use such as university research and education. ### Citation Information ```bibtex @article{fanelli_IJCV, author = {Fanelli, Gabriele and Dantone, Matthias and Gall, Juergen and Fossati, Andrea and Van Gool, Luc}, title = {Random Forests for Real Time 3D Face Analysis}, journal = {Int. J. Comput. Vision}, year = {2013}, month = {February}, volume = {101}, number = {3}, pages = {437--458} } ``` ### Contributions Thanks to [@dnaveenr](https://github.com/dnaveenr) for adding this dataset.