JLMLJob Lifetime Management Language
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Our IRM3 + CVI (p1 = 15) has 7.1% and 4.94% higher rank@1 than Quadruplet-Net [33] and JLML [34], respectively.
However, motivated to resolve the challenges for reidentification in real world (i.e., multimodal image space, and diverse impostors) IRM3 + CVI (p' = 15) has much better results than MCP-CNN [39], E2E-CAN [31], Quadruplet-Net [33], and JLML [34], while our IRM3 + CVI (p' = 15) has 1.49% higher rank@1 than DLPA [32].
Even though both JLML [34] and DLPA [32] learn deep body features with global and local body parts alignment, as well as, pose alignment, however, our IMR3 approach benefitted with transform specific metrics empowered with impostors rejection still maintained to attain better results.
To measure velocity at takeoff as the criterion reference measurement, a FP (IsoNet, JLML, Madrid, Spain) synchronized with a LPT (Isocontrol, JLML, Madrid, Spain) was used (Figure 2).