@article {380, title = {A Large Video Database for Human Motion Recognition}, year = {2011}, month = {01/2011}, abstract = {

With nearly one billion online videos viewed everyday, an emerging new frontier in computer vision research is recognition and search in video. While much effort has been devoted to the collection and annotation of large scalable static image datasets containing thousands of image categories, human action datasets lack far behind.

Here we introduce HMDB collected from various sources, mostly from movies, and a small proportion from public databases such as the Prelinger archive, YouTube and Google videos. The dataset contains 6849 clips divided into 51 action categories, each containing a minimum of 101 clips.

The actions categories can be grouped in five types:

  1. General facial actions smile, laugh, chew, talk.
  2. Facial actions with object manipulation: smoke, eat, drink.
  3. General body movements: cartwheel, clap hands, climb, climb stairs, dive, fall on the floor, backhand flip, handstand, jump, pull up, push up, run, sit down, sit up, somersault, stand up, turn, walk, wave.
  4. Body movements with object interaction: brush hair, catch, draw sword, dribble, golf, hit something, kick ball, pick, pour, push something, ride bike, ride horse, shoot ball, shoot bow, shoot gun, swing baseball bat, sword exercise, throw.
  5. Body movements for human interaction: fencing, hug, kick someone, kiss, punch, shake hands, sword fight.

Click HERE to see documentation or to download {\textquoteleft}A Large Video Database for Human Motion Recognition.{\textquoteright} \>

}, author = {E. Garrote and H. Jhuang and H. Huehne and Tomaso Poggio and T. Serre} } @article {383, title = {System for Mouse Behavior Recognition}, year = {2010}, month = {01/2010}, abstract = {

Neurobehavioural analysis of mouse phenotypes requires the monitoring of mouse behaviour over long periods of time. In this study, we describe a trainable computer vision system enabling the automated analysis of complex mouse behaviours. We provide software and an extensive manually annotated video database used for training and testing the system. Our system performs on par with human scoring, as measured from ground-truth manual annotations of thousands of clips of freely behaving mice. As a validation of the system, we characterized the home-cage behaviours of two standard inbred and two non-standard mouse strains. From these data, we were able to predict in a blind test the strain identity of individual animals with high accuracy. Our video-based software will complement existing sensor-based automated approaches and enable an adaptable, comprehensive, high-throughput, fine-grained, automated analysis of mouse behaviour.

Click here for more information and to download \>

}, author = {E. Garrote and H. Jhuang and V. Khilnani and Tomaso Poggio and T. Serre and X. Yu} }