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Evaluating Performance in Continuous Context Recognition Using Event Driven Error Characterisation

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Figure

Fig. 1. Examples (a − c) from multi-class continuous activity problem. The tables giveperformance information using standard methods: Frame errors using binary confusionmatrices of positive (P) vs
Fig. 2. (Left) flowchart of algorithm for assigning error labels to each ground truthevent, and to each prediction event: for processing ground truth events, use F,D and U,for fragmenting, deletion and underfill; for processing prediction events, use bracket
Fig. 3. Some possible error combinations for three class (is distinct from the segment labelling in that it applies to an entire event rather thanup into segmentsshows segment error pairs
Table 1. Possible segment error designations: rows represent prediction segment errors,
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