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3.4.3 Analysis of the Proposed Approach

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It can be observed that the effectiveness of the proposed approach in detecting active garments depends on the foreground information extracted and the color masks applied subsequently. If the foreground information obtained after Stage 1 (refer to Figure 3.1) fails to capture an active garment, then the recall of the active garment detection is affected. Garments with consistent color and simple patterns were more accurately detected by the proposed approach as compared to garments with multiple colors and complex patterns. Active garments were partially detected in scenarios where the customer obstructed a major portion of the garment. The proposed approach was able to identify garments of interest well, especially when the garments and wrists of a customer are in clear view.


Figure 3.10 Total duration of time for which customers were interested in a garment of a given color at a confidence threshold of 0.60.

From the data displayed in Figures 3.9 and 3.10, we can make the following key observations:

1 1. Customers spent the most time interacting with various garments having shades of blue or green color, which they found to be the most interesting compared to other colors.

2 2. Shades of red-colored garments showed the highest average confidence score consistently for all values of confidence thresholds thereby indicating that there was maximum interaction with a red-colored garment whenever one was detected as an active garment.

3 3. Customers found garments having shades of orange color the least interesting and spent the least amount of time on such garments. This indicates that they had minimum interaction with such garments which is reflected by the garments’ lowest average confidence scores as well.

The main advantage of the proposed approach is that it is able to indicate which customer is interested in which garment from a surveillance video. However, its disadvantage is that it finds it difficult to identify garments of interest correctly in highly crowded scenarios either due to the partial or complete obstruction of the garments or due to the difficulty in detecting the wrists of a person if they are obstructed by a garment.

Handbook of Intelligent Computing and Optimization for Sustainable Development

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