Wearable assistants hold the promise of supporting humans in daily tasks, which requires a persistent awareness of the objects relevant to the user. However, existing methods typically operate on short video clips or rely on offline processing, limiting their capacity for long-term understanding. In contrast, humans are able to recognize specific object instances, recall previous interactions, and opportunistically retain useful spatial information. In this paper, we propose T-EVO (Tracking in Egovision for Online Visual episodic memory), a framework for online episodic memory that processes video streams online, storing compact, queryable object memories. T-EVO integrates an object discovery module, visual tracker, and a memory module to detect, track, and store spatio-temporal data of objects. Evaluated on Ego4D, T-EVO achieves an \(81.9\%\) success rate in the oracle configuration. However, its real-world performance drops sharply to \(2.9\%\) , highlighting significant limitations in detection and tracking capabilities. It enables fast, compact retrieval—cutting storage by 24 \(\times \) and retrieval time by 9 \(\times \) - demonstrating strong potential for real-world deployment in wearable devices.