The British Machine Vision Conference (BMVC) is the British Machine Vision Association (BMVA) annual conference on machine vision, image processing, and pattern recognition.

It is one of the major international conferences on computer vision and related areas held in the UK. As its increasing popularity and quality, it has established as a prestigious event on the vision calendar.

We propose an active object recognition framework that introduces the recognition self-awareness, which is an intermediate level of reasoning to decide which views to cover during the object exploration. This is built first by learning a multi-view deep 3D object classifier; subsequently, a 3D dense saliency volume is generated by fusing together single-view visualization maps, these latter obtained by computing the gradient map of the class label on different image planes. The saliency volume indicates which object parts the classifier considers more important for deciding a class. Finally, the volume is injected in the observation model of a Partially Observable Markov Decision Process (POMDP).

In practice, the robot decides which views to cover, depending on the expected ability of the classifier to discriminate an object class by observing a specific part. For example, the robot will look for the engine to discriminate between a bicycle and a motorbike, since the classifier has found that part as highly discriminative. Experiments are carried out on depth images with both simulated and real data, showing that our framework predicts the object class with higher accuracy and lower energy consumption than a set of alternatives.

Our Partners presented the Paper “Recognition self-awareness for active object recognition on depth images”
Andrea Roberti (University of Verona); Marco Carletti (University of Verona); Francesco Setti (University of Verona)*; Umberto Castellani (University of Verona ); Paolo Fiorini (N/A); Marco Cristani (University of Verona) 

Our Partner UPC presented the paper “Incremental Tube Construction for Human Action Detection”

Harkirat Singh Behl, Michael Sapienza, Gurkirt Singh, Suman Saha, Fabio Cuzzolin, Philip H. S. Torr

In this work, we introduce a real-time and online joint-labelling and association algorithm for action detection that can incrementally construct space-time action tubes on the most challenging untrimmed action videos in which different action categories occur concurrently. In contrast to previous methods, we solve the linking, action labelling and temporal localisation problems jointly in a single pass. We demonstrate superior online association accuracy and speed (1.8ms per frame) as compared to the current state-of-the-art offline and online systems.