  The Localization of the target object for data retrieval is a key issue in
the Intelligent and Connected Transportation Systems (ICTS). However, due to
lack of intelligence in the traditional transportation system, it can take
tremendous resources to manually retrieve and locate the queried objects among
a large number of images. In order to solve this issue, we propose an effective
method to query-based object localization that uses artificial intelligence
techniques to automatically locate the queried object in the complex
background. The presented method is termed as Fine-grained and Progressive
Attention Localization Network (FPAN), which uses an image and a queried object
as input to accurately locate the target object in the image. Specifically, the
fine-grained attention module is naturally embedded into each layer of the
convolution neural network (CNN), thereby gradually suppressing the regions
that are irrelevant to the queried object and eventually shrinking attention to
the target area. We further employ top-down attentions fusion algorithm
operated by a learnable cascade up-sampling structure to establish the
connection between the attention map and the exact location of the queried
object in the original image. Furthermore, the FPAN is trained by multi-task
learning with box segmentation loss and cosine loss. At last, we conduct
comprehensive experiments on both queried-based digit localization and object
tracking with synthetic and benchmark datasets, respectively. The experimental
results show that our algorithm is far superior to other algorithms in the
synthesis datasets and outperforms most existing trackers on the OTB and VOT
datasets.
