1. Technical Field
The disclosure relates to a particle filter, and more particularly to a particular filter applied to object tracking.
2. Related Art
Usually, object tracking refers to a technique of identifying one or more objects in an image or a series of images (including a video sequence) based on different purposes. The above-mentioned tracking refers to estimation for dynamic objects. The object tracking may be applied to security, surveillance, and human resources, fabrication, or production management. Taking a surveillance system for monitoring a bank vault as an example, it is necessary to detect whether an object enters the vault, and continuously track and record the movement of the object. In another example, the object tracking may be used in a radar capable of detecting moving objects in the air to determine whether an enemy aircraft or a missile comes.
Conventionally, a particle filter may be used for performing the object tracking. The particle filter establishes an observation model according to a certain characteristic of the object, sets multiple candidates on the image, and sets a window of each of the candidates. Next, the particle filter calculates the observation model of each of the windows, and compares the observation models to estimate the movement of the object. However, when performing the object tracking, the particle filter needs to continuously access a system memory storing the image to read content of each of the windows, a great amount of system bus bandwidth is occupied and consumed. In addition, the time required for reading the system memory is longer, which results in poor processing efficiency of the particle filter.
Furthermore, with great increase of the resolution of the image to be processed or increase of the number of the objects to be tracked, the problems that a great amount of reading and writing bandwidth of the system bus is occupied and the processing efficiency is low become even worse.