Egocentric object detection is a critical aspect of robotic navigation and interaction within dynamic and complex home environments. The primary objective of this research is to explore the challenges and solutions associated with achieving temporal consistency in egocentric object detection, particularly in the scope of the Open-Vocabulary Mobile Manipulation (OVMM) challenge. This is contextualized within the HomeRobot 3D simulation environment, where a robot (Hello Robot Stretch) is tasked with navigating a household and bring an object from one place to another. The perception module of the robot is enabled by open-vocabulary object detection models, such as DETIC (Detecting Twenty-thousand Classes using Image-level Supervision).