Video tracking using Sparse and dense feature-based methods

Weyes Eyes, Inc. is developing a product that uses intelligent video analytics software to alert end users to pre-configured events as they occur in video streams from cameras set up in the home or office. The user will be able to specify which types of events to capture (doors opening, people walking by, etc.) and the software will detect these events using sparse feature-tracking and objectrecognition methods. Many state-of-the-art feature detectors and descriptors may be suitable for this task, and Weyes Eyes wishes to know which ones work best for their specific application. The research component, then, is to empirically evaluate possible feature detection/description combinations for tracking and recognition using sample videos that represent the kind of events Weyes Eyes wants to detect.

Faculty Supervisor:

Dr. Anthony Whitehead

Student:

Geoffrey Treen

Partner:

Weyes Eyes Inc.

Discipline:

Engineering

Sector:

Information and communications technologies

University:

Carleton University