Sliding-Windows for Rapid Object Class Localization: A Parallel Technique
Proceedings of the 30th DAGM symposium on Pattern Recognition
Object Detection with Discriminatively Trained Part-Based Models
IEEE Transactions on Pattern Analysis and Machine Intelligence
LIBSVM: A library for support vector machines
ACM Transactions on Intelligent Systems and Technology (TIST)
Inference and Learning with Hierarchical Shape Models
International Journal of Computer Vision
Object detection by contour segment networks
ECCV'06 Proceedings of the 9th European conference on Computer Vision - Volume Part III
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This paper proposes an architecture for an object detection system suitable for a web-service running distributed on a cluster of machines. We build on top of a recently proposed architecture for distributed visual recognition system and extend it with the object detection algorithm. As sliding-window techniques are computationally unsuitable for web-services we rely on models based on state-of-the-art hierarchical compositions for the object detection algorithm. We provide implementation details for running hierarchical models on top of a distributed platform and propose an additional hypothesis verification step to reduce many false-positives that are common in hierarchical models. For a verification we rely on a state-of-the-art descriptor extracted from the hierarchical structure and use a support vector machine for object classification. We evaluate the system on a cluster of 80 workers and show a response time of around 10 seconds at throughput of around 60 requests per minute.