text: In this paper we study the problem of estimating innercyclic time intervals within repetitive motion sequences of top-class swimmers in a swimming channel. Interval limits are given by temporal occurrences of key-poses, i.e. distinctive postures of the body. A key-pose is defined by means of only one or two specific features of the complete posture. It is often difficult to detect such subtle features directly. We therefore propose the following method: Given that we observe the swimmer from the side, we build a pictorial structure of poselets to robustly identify random support poses within the regular motion of a swimmer. We formulate a maximum likelihood model which predicts a key-pose given the occurrences of multiple support poses within one stroke. The maximum likelihood can be extended with prior knowledge about the temporal location of a key-pose in order to improve the prediction recall. We experimentally show that our models reliably and robustly detect key-poses with a high precision and that their performance can be improved by extending the framework with additional camera views.
  @cite_24: The reliable extraction of characteristic gait features from image sequences and their recognition are two important issues in gait recognition. In this paper, we propose a novel 2-step, model-based approach to gait recognition by employing a 5-link biped locomotion human model. We first extract the gait features from image sequences using the Metropolis-Hasting method. Hidden Markov Models are then trained based on the frequencies of these feature trajectories, from which recognition is performed. As it is entirely based on human gait, our approach is robust to different type of clothes the subjects wear. The model-based gait feature extraction step is insensitive to noise, cluttered background or even moving background. Furthermore, this approach also minimizes the size of the data required for recognition compared to model-free algorithms. We applied our method to both the USF Gait Challenge data-set and CMU MoBo data-set, and achieved recognition rate of 61 and 96 , respectively. The results suggest that the recognition rate is significantly limited by the distance of the subject to the camera.
  @cite_22: In this paper, a layered deformable model (LDM) is proposed for human body pose recovery in gait analysis. This model is inspired by the manually labeled silhouettes in (Z. Liu, et al, July 2004) and it is designed to closely match them. For fronto-parallel gait, the introduced LDM model defines the body part widths and lengths, the position and the joint angles of human body using 22 parameters. The model consists of four layers and allows for limb deformation. With this model, our objective is to recover its parameters (and thus the human body pose) from automatically extracted silhouettes. LDM recovery algorithm is first developed for manual silhouettes, in order to generate ground truth sequences for comparison and useful statistics regarding the LDM parameters. It is then extended for automatically extracted silhouettes. The proposed methodologies have been tested on 10005 frames from 285 gait sequences captured under various conditions and an average error rate of 7 is achieved for the lower limb joint angles of all the frames, showing great potential for model-based gait recognition.
  @cite_3: 
  @cite_11: We describe new techniques to detect and analyze periodic motion as seen from both a static and a moving camera. By tracking objects of interest, we compute an object's self-similarity as it evolves in time. For periodic motion, the self-similarity measure is also periodic and we apply time-frequency analysis to detect and characterize the periodic motion. The periodicity is also analyzed robustly using the 2D lattice structures inherent in similarity matrices. A real-time system has been implemented to track and classify objects using periodicity. Examples of object classification (people, running dogs, vehicles), person counting, and nonstationary periodicity are provided.
summary: The analysis of human gait probably plays the biggest role in the field of periodic motion research. A big focus lies on the identification of a person via his her intrinsic gait signature, for example by determining and tracking the body shape @cite_24 or through fusion of multiple gait cycles @cite_3 . More general approaches strive to recover the human body pose @cite_22 in order to retrieve a full set of gait parameters. Periodic motion in images was examined by Cutler & Davis @cite_11 , who use self similarity and frequency transformations to obtain the frequency of the periodic motion of human and animal gait.