An Improved Multi-Pedestrian Tracking System Based on Deep Neural Network

We propose a detection-based tracking system for automatically processing maritime ship inspection videos and predicting suspicious areas where cracks may exist. This system consists of two stages. Stage one uses a state-of-the-art object detection model, i.e., RetinaNet, which is customized with certain modifications and the optimal anchor setting for detecting cracks in the ship inspection images/videos. Stage two is an enhanced tracking system including two key components. The first component is a state-of-the-art tracker, namely, Channel and Spatial Reliability Tracker (CSRT), with improvements to handle model drift in a simple manner. The second component is a tailored data association algorithm which creates tracking trajectories for the cracks being tracked. This algorithm is based on not only the intersection over union (IoU) of the detections and tracking updates but also their respective areas when associating detections to the existing trackers. Consequently, the tracking results compensate for the detection jitters which could lead to both tracking jitter and creation of redundant trackers. Our study shows that the proposed detection-based tracking system has achieved a reasonable performance on automatically analyzing ship inspection videos. It has proven the feasibility of applying deep neural network based computer vision technologies to automating remote ship inspection. The proposed system is being matured and will be integrated into a digital infrastructure which will facilitate the whole ship inspection process.

Download Full-text

An Eye-Tracking System based on Inner Corner-Pupil Center Vector and Deep Neural Network

Sensors ◽

10.3390/s20010025 ◽

2019 ◽

Vol 20 (1) ◽

pp. 25

Author(s):

Mu-Chun Su ◽

Tat-Meng U ◽

Yi-Zeng Hsieh ◽

Zhe-Fu Yeh ◽

Shu-Fang Lee ◽

...

Keyword(s):

Neural Network ◽

Eye Tracking ◽

Visual Information ◽

Deep Neural Network ◽

Tracking System ◽

Estimation Algorithms ◽

Pupil Center ◽

Tracking Devices ◽

Medicine Education ◽

Center Vector

The human eye is a vital sensory organ that provides us with visual information about the world around us. It can also convey such information as our emotional state to people with whom we interact. In technology, eye tracking has become a hot research topic recently, and a growing number of eye-tracking devices have been widely applied in fields such as psychology, medicine, education, and virtual reality. However, most commercially available eye trackers are prohibitively expensive and require that the user’s head remain completely stationary in order to accurately estimate the direction of their gaze. To address these drawbacks, this paper proposes an inner corner-pupil center vector (ICPCV) eye-tracking system based on a deep neural network, which does not require that the user’s head remain stationary or expensive hardware to operate. The performance of the proposed system is compared with those of other currently available eye-tracking estimation algorithms, and the results show that it outperforms these systems.

Download Full-text