Multiple human tracking is a fundamental problem in understanding the context of a visual scene. Although both accuracy and speed are required in real-world applications, recent tracking methods based on deep learning focus on accuracy and require a substantial amount of running time. We aim to improve tracking running speeds by performing human detections at certain frame intervals because it accounts for most of the running time. The question is how to maintain accuracy while skipping human detection. In this paper, we propose a method that interpolates the detection results by using an optical flow, which is based on the fact that someone's appearance does not change much between adjacent frames. To maintain the tracking accuracy, we introduce robust interest point detection within the human regions and a tracking termination metric defined by the distribution of the interest points. On the MOT17 and MOT20 datasets in the MOTChallenge, the proposed SDOF-Tracker achieved the best performance in terms of total running time while maintaining the MOTA metric. Our code is available at https://github.com/hitottiez/sdof-tracker.

A multi-camera setup for a surveillance system enables a larger coverage area, especially when a single camera has limited monitoring capability due to certain obstacles. Therefore, for large-scale coverage, multiple cameras are the best option. In this paper, we present a method for detecting multiple objects using several cameras with large overlapping views as this allows synchronization of object identification from a number of views. The proposed method uses a graph structure that is robust enough to represent any detected moving objects by defining their vertices and edges to determine their relationships. By evaluating these object features, represented as a set of attributes in a graph, we can perform lightweight multiple object detection using several cameras, as well as performing object tracking within each camera's field of view and between two cameras. By evaluating each vertex hierarchically as a subgraph, we can further observe the features of the detected object and perform automatic separation of occluding objects. Experimental results show that the proposed method would improve the accuracy of object tracking by reducing the occurrences of incorrect identification compared to individual camera-based tracking.

Multiple human tracking is widely used in various fields such as marketing and surveillance. The typical approach associates human detection results between consecutive frames using the features and bounding boxes (position+size) of detected humans. Some methods use an omnidirectional camera to cover a wider area, but ID switch often occurs in association with detections due to following two factors: i) The feature is adversely affected because the bounding box includes many background regions when a human is captured from an oblique angle. ii) The position and size change dramatically between consecutive frames because the distance metric is non-uniform in an omnidirectional image. In this paper, we propose a novel method that accurately tracks humans with an association metric for omnidirectional images. The proposed method has two key points: i) For feature extraction, we introduce local rectification, which reduces the effect of background regions in the bounding box. ii) For distance calculation, we describe the positions in a world coordinate system where the distance metric is uniform. In the experiments, we confirmed that the Multiple Object Tracking Accuracy (MOTA) improved 3.3 in the LargeRoom dataset and improved 2.3 in the SmallRoom dataset.