Gait recognition using periodic temporal super resolution for low frame-rate videos

Robust gait recognition from extremely low frame-rate videos

“…Our method has 3 desirable properties: 1) the performance is less sensitive to the feature number used in the base classifiers; 2) it yields satisfactory results for extremely low frame-rates gait sequences under the influences of large/small gait fluctuations; 3) the performance can be further enhanced if the gallery videos have higher frame-rates. Compared with the temporal reconstruction-based methods [6,8,9], our method delivers significant improvements in terms of performance or generalization.…”

“…3. Although ERTSR outperforms our method on DB-high dataset, it should be pointed out that ERTSR assumes [6] 52 N/A PTSR [8] 44 N/A ERTSR [9] 87 N/A AG+PCA+NN 69 67 AG+NN 68 68 AG+RSM (our method) 80.40±1.35 80.60±1.26 the same motion among the gait periods, which is not applicable when there are large gait fluctuations (e.g., DB-low dataset) [9]. 4.…”

“…Based on 1fps gait videos, we compare our method with several temporal reconstruction-based, i.e., morphing-based reconstruction (Morph) [6], Periodic Temporal SR (PTSR) [8], and Example-based and Reconstruction-based Temporal SR (ERTSR) [9]. Due to the random nature of our method, we repeat each experiment 10 times and report the mean rank-1 CCRs (with the standard deviation) in Table 1.…”

“…A unified framework of example-based and reconstruction-based periodic temporal SR was proposed in [9], and it works well for gait recognition from (dual) extremely low frame-rate videos (with small gait fluctuations). Most of these works attempt to recover the high frame-rate gait sequences in the first place, yet in the applications of human identification, they either face high levels of reconstruction artifacts [6,8] for extremely low frame-rate input videos (e.g., 1fps), or assume the same motion among the gait periods, which is not applicable when there are large gait fluctuations [9].…”

“…In [7], temporal Super Resolution (SR) method was employed to construct a period of high frame-rate gait sequence based on multiple periods of low frame-rate gait sequences. Based on [7], Akae et al [8] applied an exemplar of high frame-rate image sequences to improve the temporal SR quality. A unified framework of example-based and reconstruction-based periodic temporal SR was proposed in [9], and it works well for gait recognition from (dual) extremely low frame-rate videos (with small gait fluctuations).…”