Person re-identification using geometry constrained human trajectory modeling

Person re-identification (re-ID) is the ability to associate the identity of a person observed at one time and location with the same subject when acquired at a different time and location. Trajectory forecasting is the task of predicting the likely path that a person might take to reach a destination. Contextual trajectory forecasting (CTF) leverages the 3D geometric information of the environment along with observed behavioral norms for human path prediction. Re-ID involves feature matching to find an identity in the database with similar features. The features encompass information regarding appearance of the person like color and texture, or context of the scenario like the time and location of the human subject. CTF provides a future estimate of the likely time and spatial location of previously observed subjects. Embedding this information into traditional re-ID algorithm significantly boost their performance. In this paper, re-ID is performed across non-overlapping cameras with real world human subjects. CTF is embedded into a re-identification algorithm that uses symmetry driven accumulation of local features (SDALF) [1] to evaluate the performance. Experiments suggest a significant improvement in re-identification by embedding CTF.