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Department of Computer Science and Technology

Friday, 22 September, 2023 - 13:00 to 14:00
Sachin Mathew, University of Cambridge
FW 11, William Gates Building. Zoom link:


Entomologists devote a large portion of their time manually tagging video data from camera traps in order to conduct their research. This is an enormous time, labor, and resource sink. Automation would greatly decrease the amount of work required to complete this task and would give these researchers the freedom to allocate their resources elsewhere. Despite the difficulty of this task due to the comparable scale of the insects and visual noise, the structure of these static camera videos lends itself to be interpreted by sufficiently robust machine learning models.
This work aims to address the task of tagging location specific events within insect camera traps --- such as pollination events --- at real-time or close to real-time speeds by implementing a pipeline of fast video regularization, background subtraction, and machine learning inference using the highly parallelizable and embeddable Convolutional Tsetlin Machine (CTM) architecture. This work presents a pipeline of fast regularization and background subtraction models, compared by the metrics of event detection rate, pollination image detection rate as well as pipeline iteration speed. Through this exploration a subset of operations were found such that individual pollination events were tagged with relative accuracy at fast rates with outputs easily interpretable by CTMs for an even higher detection rate at very high possible speeds in embedded systems.

Sachin Mathew is a Research Associate in the Department of Computer Science at the University of Cambridge. Their research focuses on developing non-invasive methods for gathering insect/small animal data for biodiversity and conservation tasks using computer vision.


Seminar series: 
Energy and Environment Group