One of the major factors that makes the task of tracking multiple objects difficult is frequent occlusions in congestion situations. Many existing methods in multi-object tracking by online processing have adopted a tracking-by-detection approach to perform object detection in every frame of a video and associate the object bounding box obtained by it temporally. However, with the existing method, it was not possible to track objects that could not be acquired by detectors due to occlusion. Therefore, we propose a method to shift the state of the lost target to tracked state once again by tracklet re-identification. Embeddings expressing the high dimensional appearance features of a object are acquired by using a convolutional neural network and re-identification of the tracklet is made based on the distance of the embedding vector among the tracklets. At this time, by using the masked image of the object obtained by instance segmentation as the input of the network, it is possible to perform re-identification determination robust to the background change. Further, since the re-identification determination of the tracklet pair is performed based on the distance between the low-dimensional vectors, the increase in the calculation cost due to the re-identification processing is small. Experiments were carried out using MOT16 dataset, which is a public data set, and the effectiveness of this method is shown.
|Translated title of the contribution||Online multi-object tracking with tracklet re-identification|
|Number of pages||10|
|Journal||Journal of the Institute of Image Electronics Engineers of Japan|
|Publication status||Published - 2018|
ASJC Scopus subject areas
- Computer Science (miscellaneous)
- Electrical and Electronic Engineering