Human object recognition and tracking is important in robotics and automation. The Kinect sensor and its SDK have provided a reliable human tracking solution where a constant line of sight is maintained. However, if the human object is lost from sight during the tracking, the existing method cannot recover and resume tracking the previous object correctly. In this paper, a human recognition method is developed based on colour and depth information that is provided from any RGB-D sensor. In particular, the method firstly introduces a mask based on the depth information of the sensor to segment the shirt from the image (shirt segmentation); it then extracts the colour information of the shirt for recognition (shirt recognition). As the shirt segmentation is only based on depth information, it is light invariant compared to colour-based segmentation methods. The proposed colour recognition method introduces a confidence-based ruling method to classify matches. The proposed shirt segmentation and colour recognition method is tested using a variety of shirts with the tracked human at standstill or moving in varying lighting conditions. Experiments show that the method can recognize shirts of varying colours and patterns robustly.

/pdf/human-object-recognition-using-colour-and-depth-information-58797zwnyo.pdf

Human Object Recognition Using Colour and Depth Information from an RGB-D Kinect Sensor

https://www.cscjournals.org/download/issuearchive/IJIP/volume6/IJIP_V6_I1.pdf

International journal of image processing (ijip)

The invention discloses a tracking-learning-detection (TLD)-based video object tracking method, which comprises the following steps of: (1) when a start frame of an object is tracked, generating an image sub-window according to given position and size information, and training a detector; (2) estimating the area of the object in a current frame according to the position and size information of the object in a previous frame by using a tracker; (3) detecting the current frame by using the detector, and finding all possible objects in the current frame; and (4) performing fusion processing on results of the tracker and the detector, judging whether the object exists in the current frame or not, returning to the step (3) to process the next frame if the object does not exist in the current frame, judging whether a tracking trajectory of the current frame is effective or not if the object exists in the current frame, giving the position of the object, returning to the step (2) to process the next frame if the tracking trajectory of the current frame is ineffective, finishing the online updating of the detector if the tracking trajectory of the current frame is effective, and returning to the step (2) to process the next frame. According to the method, the conventional TLD algorithm is improved to obtain a video object tracking algorithm which is more ideal than the TLD algorithm.

Tracking-learning-detection (TLD)-based video object tracking method

Digital color images are capable of presenting hue, saturation, and brightness perceptions. Therefore, quality improvement of color images should be taken into account to enhance all three stimuli....

Gradient-guided color image contrast and saturation enhancement

Intensity and edge based adaptive unsharp masking filter for color image enhancement

Identification and tracking of a moving object using computer vision techniques is important in robotic surveillance. In this paper, an adaptive colour filtering method is introduced for identifying and tracking a moving object appearing in image sequences. This filter is capable of automatically identifying the most salient colour feature of the moving object in the image and using this for a robot to track the object. The method enables the selected colour feature to adapt to surrounding condition when it is changed. A method of determining the region of interest of the moving target is also developed for the adaptive colour filter to extract colour information. Experimental results show that by using a camera mounted on a robot, the proposed methods can perform robustly in tracking a randomly moving object using adaptively selected colour features in a crowded environment.

https://downloads.hindawi.com/journals/mpe/2012/509597.pdf

Adaptive Colour Feature Identification in Image for Object Tracking

Moving object identification and tracking by computer vision plays an important role in surveillance using mobile robots. In this paper, a new method for moving object tracking using an adaptive colour filter is introduced. This method is capable of identifying the most salient colour feature in the moving object and using this colour feature to track the object. This method is also capable of adapting this selected colour feature when the surrounding condition is changed. Experimental results have shown that the proposed method can perform robustly in tracking a moving object using a robot mounted camera in a crowded environment.

Moving object tracking using an adaptive colour filter

Shadow removal plays an important role in moving object detection and tracking. In this paper, a new shadow removal method is introduced for the accurate identification of moving objects. This method firstly uses background subtraction to detect moving objects that have casted shadows. Secondly, a background reconstruction scheme is developed to create a dynamically updated background reference image. Thirdly, shadows are removed by comparing and analysing the reference image with the original video frame. This method is capable of detecting and removing shadows in different environments under different lighting conditions. Five benchmark videos typically used to test the effectiveness of shadow removal techniques and two user recorded videos have been examined using the proposed method. Experimental results have shown that the method can produce satisfactory results with regard to robustness and consistency.

Shadow removal using background reconstruction

Colour detection plays an important role for many computer vision-based applications. However, most existing colour detection methods tend to be environment dependent since slight changes of environmental factors such as illumination or shadowing effects could greatly reduce their performances. In this paper, a new colour model is introduced to allow enhanced colour detection from images, even with significantly different lighting conditions and image qualities. The proposed colour model is called the HPBr colour model. It is converted from the RGB colour model and it consists of three colour components, namely, hue (H), purity (P) and brightness (Br). This colour model can be represented in three different geometric shapes: diamond, sphere and cylinder. To assess the effectiveness of the model, two different colour detection methods have been applied onto benchmark images. Experimental results from both methods confirmed that the proposed colour model produced the best colour detection results am...

A novel colour model for colour detection

Human detection is an active topic with many interesting applications in computer vision. This paper proposes an improved human detection method to extract and segment the human body figure from a given image without prior information. This is done by detecting face, head and shoulder separately, face detection is obtained from Haar classifier while head and shoulder detections are determined from gradient maps, the final human body segmentation is generated based on gap detection and golden ratio. Experiment Results have shown that this method is capable of detecting and segmenting the human body figures robustly from various images.

Human detection using a combination of face, head and shoulder detectors

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