Built an automated inventory tracking system using SVM-based object detection to identify and count products on warehouse shelves, processing 10,000+ items daily with 97% accuracy.
A major logistics company needed to automate inventory tracking across multiple warehouse facilities. Manual counting was error-prone, time-consuming, and couldn't keep up with the scale of operations.
We implemented an SVM-based computer vision system that automatically identifies and counts products using overhead cameras and machine learning algorithms.
Achieved 97% accuracy in product identification while reducing manual labor by 70% and processing over 10,000 items daily across multiple warehouse locations.