Controlling household appliances by hand gesture is one the trends in smart home systems. Hand-gesture recognition is a developing field in human-computer interaction. In the past decades

many gesture-recognition algorithms were developed for tracking and recognizing various hand gestures. These algorithms can be categorized into two classes: static and dynamic based. Unfortunately

both types have high requirements for camera devices and environments

such as the need for RGB cameras

fixed scenes

and so on

which cause difficulty in adapting to complex home environments

especially when using wide-field cameras or experiencing heavy interferences. This paper proposes a dynamic hand-wave recognition algorithm that can respond to moving objects periodically in video sequences. A hand wave is periodic

and its frequency is relatively stable. This feature makes hand recognition possible in large scenes provided by wide-field camera. To detect this action

hand wave is regarded as a periodically moving object in this paper

and a detection algorithm that can respond to periodically changing pixels is proposed. Detection is achieved through short (SF) and long filters (LF). SF smoothens several neighbors of the current video frame only

while LF considers more frames. By comparing SF and LF outputs

current pixel state

i.e.

whether or not they are periodically changing

can be determined. By connecting these pixels

the periodically moving object (area)

which is the hand-wave candidate

is confirmed. Then

sophisticated hand-gesture recognition algorithm is applied to confirm that the candidate is indeed the hand. In practice

finding small objects in a high-resolution image that contains a complex background is one of the most challenging problems in computer vision. However

if the moving state of the object

such as periodical moving

is known in advance

detection becomes much easier. Thus

static hand gestures are not considered in this paper. To fully evaluate the performance of the proposed algorithm

it is applied while turning on or off the light in a room

and five challenging scenes

including actual household environment

are used. An experimenter waves his hand in front of the camera until light is triggered (whether switching on or off); the waving action lasts more than four seconds. If the light is triggered within four seconds

one is added to success time. Otherwise

if the light is triggered by other actions

such as talking or walking

one is added to false trigger time. Experimental results show that compared with state-of-the-art algorithms

the proposed algorithm increases its success times by about 3% and decreases the false trigger time and computation cost (time) by 44% and 0.4 seconds

respectively. Results show that the proposed method meets the needs of practical application. In addition

the method is not based on moving-object detection algorithm and has low computation costs

which means that it can run under high-resolution condition efficiently or migrate to embed system conveniently.