Web based Object Annotation Tool using a Triplet-ReID Sorting Approach

The robustness of the object detection methods has seen an increasing attention, which leads to a desire for more control over the training and testing phases. In practice, the need for labelling unique objects present on a dataset can be of help. However, manually labelling datasets of considerable size can be impractical. This paper describes an approach to improve labelling information of a dataset by supporting an object reidentification task. The primary objective is to find repeated objects in the dataset. The proposed solution relies on a web-based application that allows the user to choose which of the similar objects returned by the Triplet-ReID method are in fact the same as the query object. The effectiveness of the method was tested on a dataset with considerable object variability. Experimental results show a viable sorting performance associated with considerable speed improvement when compared to a traditional labelling approach. In fact, a dataset with 55 unique objects in a total of 1098 images would take 18 hours with a traditional tool and 12 hours with proposed one. Moreover, given the generic architecture of the developed framework, it can certainly be applied to a wide range of use cases.