Person ReIdentification Using Deep Modeling of Temporally Correlated Inertial Motion Patterns Person reidentification (reID) is among the essential components that play an integral role in constituting an automated surveillance environment. Majorly, the problem is tackled using data acquired from vision sensors using appearancebased features, which are strongly dependent on visual cues such as color, texture, etc., consequently limiting the precise reidentification of an individual. To overcome such strong dependence on visual features, many researchers have tackled the reidentification problem using human gait, which is believed to be unique and provide a distinctive biometric signature that is particularly suitable for reID in uncontrolled environments. However, imagebased gait analysis often fails to extract quality measurements of an individualxe2x80x99s motion patterns owing to problems related to variations in viewpoint, illumination (daylight), clothing, worn accessories, etc. To this end, in contrast to relying on imagebased motion measurement, this paper demonstrates the potential to reidentify an individual using inertial measurements units (IMU) based on two common sensors, namely gyroscope and accelerometer. The experiment was carried out over data acquired using smartphones and wearable IMUs from a total of 86 randomly selected individuals including 49 males and 37 females between the ages of 17 and 72 years. The data signals were first segmented into single steps and strides, which were separately fed to train a sequential deep recurrent neural network to capture implicit arbitrary longterm temporal dependencies. The experimental setup was devised in a fashion to train the network on all the subjects using data related to half of the step and stride sequences only while the inference was performed on the remaining half for the purpose of reidentification. The obtained experimental results demonstrate the potential to reliably and accurately reidentify an individual based on onexe2x80x99s inertial sensor data.

Adaptive Deep Metric Embeddings for Person ReIdentification under Occlusions Person reidentification (ReID) under occlusions is a challenging problem in video surveillance. Most of existing person ReID methods take advantage of local features to deal with occlusions. However, these methods usually independently extract features from the local regions of an image without considering the relationship among different local regions. In this paper, we propose a novel person ReID method, which learns the spatial dependencies between the local regions and extracts the discriminative feature representation of the pedestrian image based on Long ShortTerm Memory (LSTM), dealing with the problem of occlusions. In particular, we propose a novel loss (termed the adaptive nearest neighbor loss) based on the classification uncertainty to effectively reduce intraclass variations while enlarging interclass differences within the adaptive neighborhood of the sample. The proposed loss enables the deep neural network to adaptively learn discriminative metric embeddings, which significantly improve the generalization capability of recognizing unseen person identities. Extensive comparative evaluations on challenging person ReID datasets demonstrate the significantly improved performance of the proposed method compared with several stateoftheart methods.