Investigates Image data fusion techniques that combine image and track data from multiple sensors to achieve improved accuracies and more specific inferences than could be achieved by the use of a single sensor alone. Our aim is to explore the state-of-the-art image processing algorithms for achieving effective data fusion as in:
작성자 관리자날짜 2021-04-08 14:51:27조회수 31
1. Region based tracking
The idea here is to identify a connected region in the image - a "blob"-associated with each vehicle and then track it over time using a cross-correlation measure. This approach works fairs well in free-flowing traffic. However, under congested traffic conditions, vehicles partially, which makes the task of segmenting individual vehicles difficult. Such vehicles will become grouped together as one large blob in the foreground image.
2. Feature based tracking
Another approach to tracking abandons the idea of tracking objects as a whole but instead tracks sub-features such as distinguishable points or lines on the object. The advantage of this approach is that even in the presence of partial occlusion, some of the sub-features of the moving object remain visible. The technology of tracking points and line features in Tracking filtering formalism(Kalman, EK, IMM, IMMPDA etc) is well developed in the computer vision community. Since a vehicle could have multiple sub-features, the new problem then is that of grouping - what set of features belong to the same object.
3. Lip tracking
This demo is used "Active Shape Models"(ASM) algorithm. The ASM is useful to recognize a specific object. Here we want to track lip's shape as the object. First we made the Point Distribution Model about open mouth. The Open mouth's model consist of 30 points. Each point is moved toward the position with a high value of gradient.
Tracking the skeleton using the matching of joint point of previous model and current model
- Finding the object region by updating the background using adaptive background estimation
- Set the Joint point of detected region
- Make the filterbank using joint points
- Make the matching point through the similarity with detected region in frame
- Construct the skeleton using matching point