id: 05972762
dt: a
an: 05972762
au: Jung, Sung-Uk; Nixon, Mark S.
ti: Detection human motion with heel strikes for surveillance analysis.
so: Real, Pedro (ed.) et al., Computer analysis of images and patterns. 14th
    international conference, CAIP 2011, Seville, Spain, August 29‒31,
    2011. Proceedings, Part I. Berlin: Springer (ISBN
    978-3-642-23671-6/pbk). Lecture Notes in Computer Science 6854, 9-16
    (2011).
py: 2011
pu: Berlin: Springer
la: EN
cc: 
ut: heel strike detection; gait trajectory model; gradient descent; gait
ci: 
li: doi:10.1007/978-3-642-23672-3_2
ab: Summary: Heel strike detection is an important cue for human gait
    recognition and detection in visual surveillance since the heel strike
    position can be used to derive the gait periodicity, stride and step
    length. We propose a novel method for heel strike detection using a
    gait trajectory model, which is robust to occlusion, camera view and to
    low resolution which can generalize to a variety of surveillance
    imagery. When a person walks, the movement of the head is conspicuous
    and sinusoidal. The highest point of the trajectory of the head occurs
    when the feet cross. Our gait trajectory model is constructed from
    trajectory data using non-linear optimization. Then, the key frames in
    which the heel strike takes place are extracted. A Region Of Interest
    (ROI) is extracted using the silhouette image of the key frame as a
    filter. Finally, gradient descent is applied to detect maxima which are
    considered to be the time of the heel strikes. The experimental results
    show a detection rate of 95\% on two databases. The contribution of
    this research is the first use of the gait trajectory in the heel
    strike position estimation process and we contend that the approach is
    a new approach for basic analysis in surveillance imagery.
rv: