You combine bursts of intensity, where you work as hard as possible, with periods of rest that prepare you for the next effort. A real burner, this is 30 minutes of workout that will drive your body to burn calories for hours.
This 30-minute High-Intensity Interval Training (HIIT) workout uses an indoor bike to achieve fast results. Gavrila, D.: The visual analysis of human movement: A survey.A unique spin on high-intensity interval training, LES MILLS SPRINT uses the power of pedaling to push your cardiovascular fitness and calorie burn to new heights. IEEE Transactions on Pattern Analysis and Machine Intelligence 23, 257–267 (2001) IEEE Transactions on Pattern Analysis and Machine Intelligence 29, 2247–2253 (2007)īobick, A., Davis, J.: The recognition of human movement using temporal templates. Gorelick, L., Shechtman, E., Irani, M., Basri, R.: Actions as space-time shapes. ACM, New York (2000)Ĭourtney, J.: Automatic video indexing via object motion analysis. In: International Multimedia Conference, pp. Zhou, W., Vallaikal, A., Kuo, C.C.J.: Rule-based video classification system for baseketball video indexing. IEEE Transactions on Image Processing 14, 360–369 (2005) Hirakawa, K., Parks, T.: Adaptive homogeneity-directed demosaicing algorithm. ACM 22, 215–225 (1975)įischler, M., Bolles, R.: Random sample consensus: a paradigm for model fitting with application to image analysis and automated cartography. Tarjan, R.: Efficiency of a good but not linear set union algorithm. IEEE Transactions on Pattern Analysis and Machine Intelligence 25, 1337–1342 (2003) IEEE Computer Society, Washington, DC, USA (2008)Ĭucchiara, R., Grana, C., Piccardi, M., Prati, A.: Detecting moving objects, ghosts, and shadows in video streams. In: AVSS 2008: Proceedings of the 2008 IEEE Fifth International Conference on Advanced Video and Signal Based Surveillance, pp. Parks, D.H., Fels, S.S.: Evaluation of background subtraction algorithms with post-processing. This process is experimental and the keywords may be updated as the learning algorithm improves. These keywords were added by machine and not by the authors. We present heuristics to help identify the true contacts. Some videos were also seen to produce additional, spurious contacts. It successfully identifed 55 of the 56 contacts, with a mean localisation error of 1.39☑.05 pixels. We evaluated the technique using 13 videos of three sprinters. We use an array of foreground accumulators to identify short-term static pixels and a temporal analysis of the associated static regions to identify foot contacts. The algorithm exploits the variation in speed of different parts of the body during sprinting. We use this information to autonomously synchronise and overlay multiple recorded performances to provide feedback to athletes and coaches during their training sessions. We introduce a new algorithm to automatically identify the time and pixel location of foot contact events in high speed video of sprinters.
0 kommentar(er)
