  We present a content-based retrieval method for long surveillance videos both
for wide-area (Airborne) as well as near-field imagery (CCTV). Our goal is to
retrieve video segments, with a focus on detecting objects moving on routes,
that match user-defined events of interest. The sheer size and remote locations
where surveillance videos are acquired, necessitates highly compressed
representations that are also meaningful for supporting user-defined queries.
To address these challenges we archive long-surveillance video through
lightweight processing based on low-level local spatio-temporal extraction of
motion and object features. These are then hashed into an inverted index using
locality-sensitive hashing (LSH). This local approach allows for query
flexibility as well as leads to significant gains in compression. Our second
task is to extract partial matches to the user-created query and assembles them
into full matches using Dynamic Programming (DP). DP exploits causality to
assemble the indexed low level features into a video segment which matches the
query route. We examine CCTV and Airborne footage, whose low contrast makes
motion extraction more difficult. We generate robust motion estimates for
Airborne data using a tracklets generation algorithm while we use Horn and
Schunck approach to generate motion estimates for CCTV. Our approach handles
long routes, low contrasts and occlusion. We derive bounds on the rate of false
positives and demonstrate the effectiveness of the approach for counting,
motion pattern recognition and abandoned object applications.
