20 research outputs found
Dual Embedding Expansion for Vehicle Re-identification
Vehicle re-identification plays a crucial role in the management of
transportation infrastructure and traffic flow. However, this is a challenging
task due to the large view-point variations in appearance, environmental and
instance-related factors. Modern systems deploy CNNs to produce unique
representations from the images of each vehicle instance. Most work focuses on
leveraging new losses and network architectures to improve the descriptiveness
of these representations. In contrast, our work concentrates on re-ranking and
embedding expansion techniques. We propose an efficient approach for combining
the outputs of multiple models at various scales while exploiting tracklet and
neighbor information, called dual embedding expansion (DEx). Additionally, a
comparative study of several common image retrieval techniques is presented in
the context of vehicle re-ID. Our system yields competitive performance in the
2020 NVIDIA AI City Challenge with promising results. We demonstrate that DEx
when combined with other re-ranking techniques, can produce an even larger gain
without any additional attribute labels or manual supervision
Visualizing the Invisible: Occluded Vehicle Segmentation and Recovery
In this paper, we propose a novel iterative multi-task framework to complete
the segmentation mask of an occluded vehicle and recover the appearance of its
invisible parts. In particular, to improve the quality of the segmentation
completion, we present two coupled discriminators and introduce an auxiliary 3D
model pool for sampling authentic silhouettes as adversarial samples. In
addition, we propose a two-path structure with a shared network to enhance the
appearance recovery capability. By iteratively performing the segmentation
completion and the appearance recovery, the results will be progressively
refined. To evaluate our method, we present a dataset, the Occluded Vehicle
dataset, containing synthetic and real-world occluded vehicle images. We
conduct comparison experiments on this dataset and demonstrate that our model
outperforms the state-of-the-art in tasks of recovering segmentation mask and
appearance for occluded vehicles. Moreover, we also demonstrate that our
appearance recovery approach can benefit the occluded vehicle tracking in
real-world videos