10 research outputs found
Deep Learning-Based SOLO Architecture for Re-Identification of Single Persons by Locations
Analyzing and judging of captured and retrieved images of the targets from the surveillance video cameras for person re-identification have been a herculean task for computer vision that is worth further research. Hence, re-identification of single persons by locations based on single objects by locations (SOLO) model is proposed in this paper. To achieve the re-identification goal, we based the training of the re-identification model on synchronized stochastic gradient descent (SGD). SOLO is capable of exploiting the contextual cues and segmenting individual persons by their motions. The proposed approach consists of the following steps: (1) reformulating the person instance segmentation as: (a) prediction of category and (b) mask generation tasks for each person instance, (2) dividing the input person image into a uniform grids, i.e., G×G grid cells in such a way that a grid cell can predict the category of the semantic and masks of the person instances provided the center of the person falls into the grid cell and (3) conducting person segmentation. Discriminating features of individual persons are obtained by extraction using convolution neural networks. On person re-identification Market-1501 dataset, SOLO model achieved mAP of 84.1% and 93.8% rank-1 identification rate, higher than what is achieved by other comparative algorithms such as PL-Net, SegHAN, Siamese, GoogLeNet, and M3L (IBN-Net50). On person re-identification CUHK03 dataset, SOLO model achieved mAP of 82.1 % and 90.1% rank-1 identification rate, higher than what is achieved by other comparative algorithms such as PL-Net, SegHAN, Siamese, GoogLeNet, and M3L (IBN-Net50). These results show that SOLO model achieves best results for person re-identification, indicating high effectiveness of the model. The research contributions are: (1) Application of synchronized stochastic gradient descent (SGD) to SOLO training for person re-identification and (2) Single objects by locations using semantic category branch and instance mask branch instead of detect-then-segment method, thereby converting person instance segmentation into a solvable problem of single-shot classification
Re-ID done right: towards good practices for person re-identification
Training a deep architecture using a ranking loss has become standard for the
person re-identification task. Increasingly, these deep architectures include
additional components that leverage part detections, attribute predictions,
pose estimators and other auxiliary information, in order to more effectively
localize and align discriminative image regions. In this paper we adopt a
different approach and carefully design each component of a simple deep
architecture and, critically, the strategy for training it effectively for
person re-identification. We extensively evaluate each design choice, leading
to a list of good practices for person re-identification. By following these
practices, our approach outperforms the state of the art, including more
complex methods with auxiliary components, by large margins on four benchmark
datasets. We also provide a qualitative analysis of our trained representation
which indicates that, while compact, it is able to capture information from
localized and discriminative regions, in a manner akin to an implicit attention
mechanism