Abstract:
Person Re-Identification (Re-ID) is an important assignment in the digital world where
the access to the surveillance systems in a constrained or in-the-wild environment is possible.
Person Re-ID in a video-based scenario is considered as state-of-the-art (SOTA) way of going
forward to get fruitful results from this application in a non-overlapping surveillance feed
scenario. In this thesis, four SOTA works are identified in terms of what architecture the
respective authors have worked with and gained the maximum accuracy when compared with the
accuracy gained by the same type of architectures. The provided hyper-parameters are changed
to see what positive/negative effect different sets of hyper-parameters have on the SOTA
architectures. Additionally, in this thesis an already used loss function in a different field is
identified that can have positive/fruitful effects in Person Re-identification field. Following
contributions are made in this work. (1) Four SOTA along-with a strong backbone network with
pre-processed input ways have been identified. (2) Different sets of hyper-parameters are
reported after extensive experimentation and generated better results than the original hyperparameters reported with the architecture. (3) A loss function is identified that is added in the
SOTA, and the baseline architectures and the modified networks are trained again. The results
are improved on MARS [9] dataset on which the SOTA are trained and reported. (4) The SOTA
architectures are evaluated on an unseen dataset (P-DESTRE) with two different types of
experiments performed (i) On Overall Dataset and (ii) On Cleaned Dataset and the accuracy of
all SOTA and modified SOTA are reported in terms of mAP. The best performing model on
MARS [9] dataset was modified MGH (added loss function) that reported a maximum of
83.61% mAP. On P-DESTRE, the best performing architecture on the cleaned dataset was the
original MGH [5] that reported a maximum of 95.69% mAP. The trainings and evaluation are
explained in their respective sections.
