Salient features, combined detectors and image flipping: an approach to Haar cascades for recognising horses and other complex, deformable objects
ACM (Association for Computing Machinery)
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Reason for embargo
Under embargo until end of conference
The author describes a new ‘shortcut’ approach to automatically detecting horses in still images and video: salient features, combining and flipping. Horses are complex, deformable (non-rigid) target objects with high levels of intra-class shape variability. A prototype Haar cascade detector was trained to detect what the author calls a ‘salient feature’. This a distinctive, minimally changing physical attribute that is easily recognisable from multiple viewpoints. The detector’s target object is: ‘horse ears’ and it only required a total training time of 91 minutes. It was evaluated in combination with an existing, ‘asymmetric’ detector (trained only to recognise right-facing horses). By combining the existing horse detector with the author’s salient feature ears detector, the hit rate for true positives was increased by 50% (relative to the existing detector’s performance). Flipping each test image (or video frame) around its vertical axis increased the hit rate by 83% (relative to the unflipped results) for the existing, asymmetric detector, when tested on an image dataset of horses facing in both directions.
The work described in this paper builds on an exploratory project funded by the EPSRC under Platform Grant 'Living with Digital Ubiquity ' reference: EP/M000877/1.
This is the author accepted manuscript. The final version is available from ACM via the DOI in this record.
ACI2017: Fourth International Conference on Animal-Computer Interaction , 21-23 November 2017, Milton Keynes, UK
Place of publication
New York, USA