Patent Publication Number: US-10789688-B2

Title: Method, device, and system for enhancing changes in an image captured by a thermal camera

Description:
CROSS-REFERENCE TO RELATED APPLICATION 
     The present application claims priority to EP 18213427.0, filed Dec. 18, 2018, the entire contents of which are incorporated herein by reference. 
     TECHNICAL FIELD 
     The present invention relates to the field of processing thermal images. Specifically, it relates to a method, a device, and a system for enhancing changes in an image of an image sequence captured by a thermal camera. 
     BACKGROUND 
     Thermal cameras are nowadays often used for surveillance purposes. Thermal cameras are great detectors which can be used to both detect and classify objects at long distances and under difficult circumstances, often much better than a visual light camera. However, due to a generally lower resolution of thermal cameras compared to visual light cameras, this detection potential is hard to utilize in automatic systems such as automated motion detection systems. Because of the lower resolution, a small or distant object in the scene may only cover one or a few pixels of the captured thermal images. When motion in the scene is detected from the thermal images, the small or distant object may therefore easily be mistaken for noise instead of being detected as a moving object. In addition, most motion detection engines are optimized for visual camera images and not for thermal camera images. 
     These problems are recognized in EP3016383 A1. In that document it is proposed to enhance pixels of an image in an image sequence that have changed since the previous image in the image sequence. In that way, a small or distant moving object in the scene will appear as larger in the image. That is, the object will cover an increased amount of pixels in the image. It is therefore less likely to be mistaken for noise and is more likely to be detected as a moving object. 
     EP3016383 A1 takes the simplistic approach of enhancing the pixels that have changed since the previous frame by gaining or adding an offset to the pixel values. However, in order to further improve the chances of correctly detecting moving objects from a thermal image sequence, there is a need for methods which serve to even further enhance changes in the thermal image sequence. 
     SUMMARY OF THE INVENTION 
     In view of the above, it is thus an object of the invention to mitigate the above problems and further improve enhancement of changes in a thermal image sequence. 
     According to a first aspect, the above object is achieved by a method for enhancing changes in an image of an image sequence captured by a thermal camera. The method comprises: 
     receiving an image which is part of an image sequence captured by a thermal camera, 
     identifying pixels in the image that have changed in relation to another image in the image sequence by comparing the image to the other image in the image sequence, 
     determining, based on intensity values of the identified pixels, a function for redistributing intensity values of pixels in the image, wherein the function has a maximum for a first intensity value in a range of the intensity values of the identified pixels, and decays with increasing distance from the first intensity value such that the function assigns lower values to intensity values outside of the range than to the first intensity value, and 
     redistributing intensity values of pixels in the image that have changed in relation to the other image and intensity values of pixels in the image that have not changed in relation to the other image by using the determined function, thereby enhancing changes in the image. 
     With this method, pixels which have changed since another image in the sequence are identified. On basis of intensity values of the identified pixels, a function is determined which later on is used when redistributing intensity values in the image. By doing so, the function may be tailored after the intensity values of the changed pixels in the image such that these intensity values are emphasized in the image in a desired way upon performing the redistribution action. 
     The function is used to redistribute the intensity values of changed as well as non-changed pixels in the image. In other words, intensity values of both pixels that have changed in relation to the other image and pixels that have not changed in relation to the other image are redistributed. This may include all pixels in the image, or at least a majority of the pixels in the image. Due to its shape, with the maximum within the range of intensity values of the changed pixels and the decay with increasing distance from the maximum, the function serves several purposes. More specifically, it does not only tend to give higher weight to pixel intensities corresponding to those of the changed pixels, but it also tends to suppress pixel intensities which are not within the intensity range of the changed pixels. In that way, the contrast is increased between pixels having intensity values within the intensity range of the changed pixels and pixels having intensity values outside of that intensity range. As a result, moving objects will be easier to distinguish and detect in the enhanced image compared to the original image, for example, by using a motion detector which is subsequently applied in the processing of the image. 
     By the range of the intensity values of the identified pixels is generally meant the interval of intensity values between the minimum intensity value and the maximum intensity value of the identified pixels. 
     The function for redistributing intensity values of pixels in the image is a function which maps an intensity value in a pixel to a weighting factor. The function may thus also be considered as a function for weighting intensity values of pixels in the image. The function may be used to redistribute intensity values of all pixels in the image by weighting the intensity value of each pixel in the image by the weighting factor assigned to the intensity value by the function. 
     By a decay of the function with increasing distance is meant that the function decreases with increasing distance. The decrease may be strict or non-strict. However, since the function assigns lower values to intensity values outside of the range than to the first intensity value which is inside the range, the function strictly decreases with distance at least for a portion of the range of intensity values of the identified pixels. 
     The rate of decay of the function may be determined based on a variation of the intensity values of the identified pixels, wherein a higher variation gives a lower rate of decay. In this way, the function may be adapted to the variation of the intensity values of the changed pixels. More specifically, one may avoid that the rate of decay becomes too fast in relation to the variation of the intensity values of the identified pixels, since that would cause some of the changed pixels to not be properly enhanced. Conversely, one may avoid that the rate of decay is too slow in relation to the variation of the intensity values of the identified pixels, since that would cause a poor contrast between pixels having intensity values within the intensity range of the changed pixels and pixels having intensity values outside of that intensity range. By way of example, if the intensity values of the identified pixels show up as a narrow peak in a histogram, a faster decaying function may be used compared to if the intensity values of the changed pixels show up as a wider peak in the histogram. 
     The variation of the intensity values of the identified pixels may be determined based on the width of the range of intensity values of the identified pixels. Alternatively, the variation of the intensity values of the identified pixels may be calculated based on a standard deviation of the intensity values of the identified pixels. 
     The rate of decay of the function may further be based on a variation of intensity values of pixels in the image that are not identified as having changed in relation to the other image, wherein a higher variation of intensity values of pixels in the image that are not identified as having changed in relation to the other image gives a lower rate of decay. Also in this case, the variation may be calculated based on a width of a range or a standard deviation of the intensity values of the concerned pixels. In this way, not only the variation of the intensity values of the changed pixels is taken into account, but also the variation of the intensity values of the non-changed pixels. For example, the relation between the variations of the intensity values of the non-changed pixels and the changed pixels may be taken into account when setting the rate of decay of the function. If the variation of the intensity values of the non-changed pixels is large in relation to the variation of the changed pixels it may, for instance, be beneficial to have a function with a lower rate of decay. Such a situation may occur when only a few pixels have been identified as having changed in relation to the other image. It has been found that by estimating the variation from those few changed pixels (if even possible), one may arrive at a rate of decay which is too fast to allow for proper enhancement of changes in the image. By taking the variation of also the non-changed pixels into account, one may instead arrive at a function with a lower rate of decay, thereby improving the enhancement of changes in the image. 
     Different approaches may be taken to select the first intensity value for which the determined function has a maximum. 
     In one approach, the first intensity value corresponds to a mode of the intensity values of the identified pixels. The mode is the most frequent intensity value of the identified pixels. The mode may be extracted from a histogram of the intensity values. 
     In another approach, the first intensity value corresponds to a mid-point of the range of intensity values of the identified pixels. This is a simple way of setting the first intensity value that does not require calculation of a histogram. 
     In yet another approach, the first intensity value corresponds to a mean value of the intensity values of the identified pixels. This also provides a simple way of setting the first intensity value. 
     Further approaches include the median or the center of gravity of the distribution of the intensity values of the identified pixels. 
     Which approach to choose in a particular situation may, for example, depend on the shape of the distribution of the intensity values of the identified pixels. For a symmetric shape of the distribution the mean value may do, while for more complicated shapes of the distribution the mode or the mid-point of the range may be preferred. In some cases, the choice may depend on where the intensity values of the identified pixels are located on an intensity scale. For example, if the range of the intensity values of the changed pixels is located at the upper end of the intensity scale, it may be beneficial to set the first intensity value to be at the lower end of the range of intensity values of the changed pixels, and vice versa. 
     The function may be symmetric around the first intensity value. This may be appropriate if the distribution of the intensity values of the identified pixels is approximately symmetric. 
     Alternatively, the function may be skewed around the first intensity value. By the function being skewed is meant that one of its tails is heavier than the other tail. A reason of using a skewed function is that it allows for putting more emphasize on intensity values which are higher than the first intensity than on intensity values which are lower than the first intensity, or vice versa. 
     For example, the function may be skewed to have a heavier tail for higher intensity values than for lower intensity values. This may, for instance, be used in an application dealing with detecting human beings or animals in an environment which is colder than the body temperature of the human or the animal. Having a heavier tail for higher intensity values would then allow for putting more emphasize on intensity values associated with the human beings compared to the background. 
     In the reverse situation where human beings or animals in a warmer environment are to be detected, the heavier tail may instead be located for the lower intensity values. 
     In practice, a temperature sensor, e.g., located in the thermal camera could be used to determine the ambient temperature and the side of the skewness may be determined on basis of the measured ambient temperature. If the ambient temperature is lower than a typical temperature of the objects of interest, the skewness of the function may be set to have a heavier tail for higher intensity values than for lower intensity values, and vice versa. Ambient temperature may be measured using a thermal detector of bolometer type in the thermal camera. 
     The function may be a parametric function. For example, the function may be a Gaussian function (in a symmetric case) or a skewed Gaussian function (in a non-symmetric case). This is advantageous in that the function easily may be determined from a few parameters which are extracted from the intensity values of the identified pixels. 
     The step of identifying pixels in the image that have changed in relation to another image in the image sequence may include identifying pixels in the image that have an intensity value which differs by more than a threshold value from an intensity value of a corresponding pixel of the other image in the image sequence. By applying a threshold, small and insignificant changes (such as noise) may be excluded. 
     According to a second aspect, there is provided a device for enhancing changes in an image of an image sequence captured by a thermal camera, comprising: 
     a receiver configured to receive an image which is part of an image sequence captured by a thermal camera, 
     a change detector configured to identify pixels in the image that have changed in relation to another image in the image sequence by comparing the image to the other image in the image sequence, 
     a redistribution function determiner configured to determine, based on intensity values of the identified pixels, a function for redistributing intensity values of pixels in the image, wherein the function has a maximum for a first intensity value in a range of the intensity values of the identified pixels, and decays with increasing distance from the first intensity value such that the function assigns lower values to intensity values outside of the range than to the first intensity value, and 
     an intensity value redistributor configured to redistribute intensity values of pixels in the image that have changed in relation to the other image and intensity values of pixels in the image that have not changed in relation to the other image by using the determined function, thereby enhancing changes in the image. 
     According to a third aspect, there is provided a system for enhancing changes in an image of an image sequence captured by a thermal camera, comprising: 
     a thermal camera configured to capture an image sequence, 
     a device according to the second aspect arranged to receive images of the image sequence captured by the thermal camera. 
     According to a fourth aspect, there is provided a computer-readable storage medium having computer code instructions stored thereon which, when executed by a processor, causes the processor to carry out the method of the first aspect. The computer-readable storage medium may be a non-transitory computer-readable storage medium. 
     The second, third, and fourth aspects may generally have the same features and advantages as the first aspect. It is further noted that the invention relates to all possible combinations of features unless explicitly stated otherwise. 
    
    
     
       BRIEF DESCRIPTION OF THE DRAWINGS 
       The above, as well as additional objects, features and advantages of the present invention, will be better understood through the following illustrative and non-limiting detailed description of embodiments of the present invention, with reference to the appended drawings, where the same reference numerals will be used for similar elements, wherein: 
         FIG. 1  schematically illustrates a system for enhancing changes in an image sequence captured by a thermal camera according to embodiments. 
         FIG. 2  schematically illustrates a device for enhancing changes in an image sequence captured by a thermal camera according to embodiments. 
         FIG. 3  is a flow chart of a method for enhancing changes in an image sequence captured by a thermal camera according to embodiments. 
         FIG. 4  schematically illustrates how to identify pixels in a current image that have changed in relation to another image according to embodiments. 
         FIG. 5 a    shows an example of a histogram of intensity values of a current image. 
         FIG. 5 b    shows an example of a histogram of intensity values of a current image that have changed in relation to another image. 
         FIG. 5 c    shows an example of a histogram of intensity values of an image in which changes have been enhanced. 
         FIG. 6  schematically illustrates various examples of functions which may be used for redistributing intensity values of pixels in an image according to embodiments. 
     
    
    
     DETAILED DESCRIPTION OF EMBODIMENTS 
     The present invention will now be described more fully hereinafter with reference to the accompanying drawings, in which embodiments of the invention are shown. 
       FIG. 1  illustrates a system  100 . The system  100  may be used for enhancing changes in an image of an image sequence captured by a thermal camera  102 . The system  100  comprises the thermal camera  102  and an image enhancement device  104 . The system  100  may also comprise a motion detector  106 . The image enhancement device  104  may be an integral part of the thermal camera  102 . For example, the image enhancement device  104  may be integrated in an image processing pipeline of the thermal camera  102 . As such, it does not need to be an isolated component of the thermal camera, but it may share components, for instance a processor, with other parts of the thermal camera. Alternatively, and as shown in  FIG. 1 , the image enhancement device  104  may be provided separately from the thermal camera  102 . 
     The system  100  may be used for monitoring or surveillance purposes. The thermal camera may hence be a monitoring camera. The system  100  may, for example, be used for motion detection in scenes which are static most of the time, such as for perimeter surveillance. When used for such purposes, the system  100  serves to improve the performance of the motion detection by enhancing changes in the images, thereby making them more visible and easier to detect for the motion detector  106 . 
     The thermal camera  102  utilizes a thermal sensor to visualize heat differences within the scene. As is known in the art, a thermal sensor detects thermal energy emitted by objects in the scene and converts the detected thermal energy into electric signals to produce a thermal image or a thermal video, i.e., a sequence of thermal images  103 . The intensity values of the pixels of the so produced images are thus indicative of the thermal energy emitted by the objects in the scene, which in turn depends on the temperature of the emitting objects. The thermal camera  102  may hence provide a sequence of thermal images  103  in which a warm object, such as a human being, or an animal, stands out against a surrounding, cooler, background, or vice versa. Examples of other objects for which the invention may be used are motorized vehicles of different types, such as cars or drones. By way of example,  FIG. 5 a    schematically illustrates a histogram  500  of intensity values of a thermal image which depicts human beings in a cooler background. The human beings show up as a peak  502  in the histogram  500 . 
     The system  100  is capable of enhancing changes in images of the image sequence  103  regardless of whether the thermal camera  102  is absolutely calibrated or not. For an absolutely calibrated thermal camera the intensity values of the thermal images  103  are calibrated with respect to the temperature scale. This implies that temperatures may directly be read out from the intensity values of the thermal images  103 . 
     The thermal camera  102  is arranged to provide the sequence of thermal images  103  to the image enhancement device  104 . In case the image enhancement device  104  is separate from the thermal camera  102 , the sequence of thermal images  103  may be transmitted from the thermal camera  102  to the device  104  via a cable or wirelessly over a network. The image enhancement device  104 , in turn, processes images of the sequence  103  to produce an enhanced sequence of images  105  in which changes in the images are enhanced. As a result of the processing, changes in the images will be enhanced so as to stand out more clearly from the surrounding background. For example, the processing causes an increase in contrast between changed pixels and the background in the image. The image enhancement device  104  may be located early in the processing chain or pipeline of the thermal camera  102 . In some embodiments, the image enhancement device  104  even operates on image raw data. Early in the processing chain the image data is typically represented at a high resolution, such as at a high spatial resolution and/or a high bit resolution (more bits per pixel). Later on in the processing chain (such as prior to motion detection), the resolution of the image data may be down scaled, e.g., by reducing the spatial resolution and/or the bit resolution. By enhancing changes at an early stage, at the higher resolution, also changes that otherwise would be lost by the down scaling may be detected and enhanced. In that way, the enhancement of the changes has a higher impact if it is made early in the processing chain. 
     In some cases, this may be utilized to improve the detection of moving objects in the sequence of images  103  captured by the thermal camera  102 . In more detail, the enhanced sequence of images  105  may be input to a motion detector  106 . The motion detector  106  may be a standard, commercially available, motion detector which implements a known motion detection algorithm. The motion detector  106  need not be specifically adapted to process thermal images. Rather, as is often the case, the motion detector  106  may be designed to detect motion in images captured by visual light cameras. Since changes in the images are enhanced in the enhanced image sequence  105 , they are easier to detect for the motion detector  106  compared to if the original image sequence  103  instead had been provided at its input. For example, and as described above, the chances are improved of detecting small or distant moving objects in the scene that only cover one or a few pixels in the thermal images. The processing carried out by the image enhancement device  104  may hence in some cases be seen as a pre-processing of the sequence of thermal images  103  prior to motion detection. 
     In other cases, the enhancement of changes in the image may be utilized to improve the identification of moving objects in the sequence of images  103 , such as by performing shape detection. The system  100  may therefore include a shape detector instead of, or in addition, to the motion detector  106 . By using a shape detector, it is possible to not only detect motion, but also to identify what kind of object is moving. 
       FIG. 2  illustrates the image enhancement device  104  in more detail. The image enhancement device  104  comprises a receiver  202 , a change detector  204 , a redistribution function determiner  206 , and an intensity value redistributor  208 . 
     The image enhancement device  104  thus comprises various components  202 ,  204 ,  206 ,  208  which are configured to implement the functionality of the device  104 . In particular, each illustrated component corresponds to a functionality of the device  104 . Generally, the device  104  may comprise circuitry which is configured to implement the components  202 ,  204 ,  206 ,  208  and, more specifically, their functionality. 
     In a hardware implementation, each of the components  202 ,  204 ,  206 ,  208  may correspond to circuitry which is dedicated and specifically designed to provide the functionality of the component. The circuitry may be in the form of one or more integrated circuits, such as one or more application specific integrated circuits or one or more field-programmable gate arrays. By way of example, the change detector  204  may thus comprise circuitry which, when in use, identifies pixels in the image that have changed in relation to another image in the image sequence. 
     In a software implementation, the circuitry may instead be in the form of a processor, such as a microprocessor, which in association with computer code instructions stored on a (non-transitory) computer-readable medium, such as a non-volatile memory, causes the device  104  to carry out any method disclosed herein. Examples of non-volatile memory include read-only memory, flash memory, ferroelectric RAM, magnetic computer storage devices, optical discs, and the like. In a software case, the components  202 ,  204 ,  206 ,  208  may thus each correspond to a portion of computer code instructions stored on the computer-readable medium, that, when executed by the processor, causes the device  104  to carry out the functionality of the component. 
     It is to be understood that it is also possible to have a combination of a hardware and a software implementation, meaning that the functionality of some of the components  202 ,  204 ,  206 ,  208  are implemented in hardware and others in software. 
     The operation of the image enhancement device  104  when enhancing changes in an image of the image sequence  103  captured by the thermal camera  102  will now be described with reference to  FIGS. 1, 2 , and the flow chart of  FIG. 3 . 
     In step S 02  of  FIG. 3 , the receiver  202  of the image enhancement device  104  receives the image sequence  103  captured by the thermal camera  102 . The images of the sequence  103  may be received sequentially as they are captured by the thermal camera  102 . Specifically, the receiver  202  receives a current image  103   a  which is about to be enhanced, and another image  103   b  of the sequence  103 . The other image  103   b  may be previous to the current image  103   a  in the image sequence  103 , or it may be later than the current image  103   a  in the image sequence  103 . The other image  103   b  may be adjacent to the current image  103   a  in the sequence  103 . For example, it may be the immediately preceding image to the current image  103   a  in the sequence  103 . However, it may in principle be any earlier or later image in the sequence  103 . 
     To exemplify,  FIG. 4  illustrates a current  103   a  and a previous  103   b  image in the sequence  103 . Each of the previous image  103   b  and the current image  103   a  depicts a first object  402 , a second object  404 , and background  406 . In the example, the objects  402 ,  404  are illustrated as human beings. The first object  402  has moved between the previous frame  103   b  and the current frame  103   a , while the second object  404  is stationary. To illustrate this fact, the position of the first object  402  in the previous frame  103   b  is indicated in the current image  103   a  by means of dashed lines. 
     The current image  103   a  and the other image  103   b  are input to the change detector  204 . In step S 04 , the change detector  204  identifies pixels in the image  103   a  that have changed in relation to the other image  103   b . For that purpose, the change detector  204  may compare the current image  103   a  to the other image  103   b  pixel by pixel to see which pixels have changed. In that way, changes in the image  103   a  may quickly be identified. To determine which pixels have changed, the change detector  204  may apply a threshold. Pixels having an intensity value which has changed by more than the threshold since the previous frame may be identified as pixels that have changed. The other pixels are identified as pixels that have not changed. The value of the threshold may be set depending on the variability of the noise at the thermal sensor. Specifically, for thermal cameras, the threshold may be related to the Noise Equivalent Temperature Difference, NETD, which is a signal-to-noise ratio for thermal cameras. For example, a threshold which is equal to twice the standard deviation of the sensor noise may be used. In that way, most of the changes in the image  103   a  identified by the change detector  204  will correspond to changes in the scene rather than being a result of noise. 
     Alternatively, or additionally, the change detector  204  may remove isolated pixels or small groups of contiguous pixels from the set of identified pixels. For example, the change detector  204  may remove groups of pixels from the set of identified pixels if the number of contiguous pixels in the groups is smaller than a threshold. In that way, the set of identified pixels only includes groups of contiguous pixels where the number of contiguous pixels in each group is larger than or equal to the threshold. 
     The change detector  204  may also compare more than two images, such as three images. Pixels which have changed between each pair of consecutive images may be identified as changed pixels. 
     The change detector  204  may send an indication  210  of which pixels in the current image  103   a  that have changed in relation to the other image  103   b  to the redistribution function determiner  206 . The indication may, for example, be in the form of a mask which takes one value (such as the value “1”) for pixels which are found to have changed, and another value (such as the value “0”) for pixels which are not found to have changed. 
     Returning to the example of  FIG. 4 , the change detector  204  would in step S 04  find that the pixels in the current image  103   a  that correspond to the first object  402  when positioned as in the current image  103   a  or as in the previous image  103   b  have changed. The change detector  204  would further find that the pixels in the current image  103   a  that correspond to the second object  404  or the background  406  have not changed, since the second object  404  as well as the background is stationary. Based on its findings, and as shown in  FIG. 4 , the change detector  204  may produce an indication  210  of which pixels  408  have changed since the previous image. The changed pixels  408  are illustrated by black in  FIG. 4 . The example of  FIG. 4  is simplified to illustrate the principles of the invention. In a real-world situation, not all pixels inside of the first object  402  would be identified as having changed since many of the pixels inside of the object  402  have essentially the same temperature and the value of a pixel inside of the object would therefore be essentially the same even if the object has moved. However, most pixels at the boundaries of the object  402  would be identified as having changed since these pixels represent the change from background pixels to object pixels, and vice versa. 
     In the above example, the change detector  204  identified all pixels in the current image  103   a  that had changed by more than a threshold value in relation to the other image  103   b  as changing pixels. In other embodiments, the change detector  204  may also take the direction of change into account. For example, the change detector  204  may only identify pixels in the current image  103   a  that have increased by more than a threshold value in relation to the other image  103   b  as changed pixels. This may be relevant when objects which are warmer than the background are of interest. Alternatively, the change detector  204  may only identify pixels in the current image  103   b  that have decreased by more than a threshold value in relation to the other image  103   b  as changed pixels. This may be relevant when objects which are cooler than the background are of interest. 
     It has been found that it is not necessary to take the direction of change (increase or decrease in intensity value) into account when identifying changed pixels. The invention still works satisfactorily in the situation shown in  FIG. 4  where all pixels that have changed, regardless of the direction of change, are identified as changed pixels. This may be particularly useful when it is known whether objects which are warmer or cooler than the background are of interest. Some of these changing pixels would, in principle, belong to the background in the current image  103   a . However, in reality, there are no sharp contours of the objects due to diffraction. This means that pixels at the contours of the objects will be a mix of background and object pixels. As long as the movement between two frames is in the same order of magnitude as the diffraction (which is typically a few pixels, such as 2-3 pixels, in each direction) it is not necessary to take the direction of change into account when identifying the changed pixels. The changed pixels at the contours of the objects will anyway be a mix of object and background pixels and may be included with good results. 
     In step S 06 , the redistribution function determiner  206  proceeds to determine a function  212  which may be used when redistributing intensity values of pixels in the current image  103   a . The determination of the function  212  is based on the intensity values of the changed pixels  408  as identified by the change detector  204 . In order to do so, the redistribution function determiner  206  may use the indication  210  produced by the change detector  204  to extract the intensity values of the changing pixels  408  from the current image  103   a . For example, in case the indication  210  is provided as a mask, this may be performed by a masking procedure. 
       FIG. 5 a    schematically illustrates a histogram  500  of the intensity values of all pixels in the current image  103   a . The histogram  500  shows how the intensity values of the pixels in the current image  103   a  are distributed. The histogram  500  has a first peak  501  at a lower part of the intensity scale and a second peak  502  at an upper part of the intensity scale. The first peak  501  is associated with the thermal energy emitted by the background  406  in the scene, and the second peak  502  is associated with the thermal energy emitted by the first object  402  and the second object  404 . This is thus an example where the objects  402  and  404  are warmer than the background  406 . However, it is understood that there could be situations where it is the other way around, i.e., that the objects are cooler than the background.  FIG. 5 b    illustrates a histogram  510  of the intensity values of the changed pixels  408  of the current image  103   a . The histogram  510  shows how the intensity values of the changed pixels  408  in the current image  103   a  are distributed. In this case, the histogram  510  has a single peak  512  which mostly corresponds to thermal energy emitted by the first, moving, object  402 . As can be seen from the histogram  510 , the intensity values of the changed pixels  408  take values in a range  514  between a minimum intensity value “Imin” and a maximum intensity value “Imax”. 
     The redistribution function determiner  206  may generally analyse the intensity values of the changed pixels  408 , such as the distribution of the intensity values or properties thereof, to determine the function  212 .  FIG. 6  illustrates what the function  212  may look like by way of five example functions  212   a ,  212   b ,  212   c ,  212   d ,  212   e . The shape of the histogram  510  is also shown in  FIG. 6  for reference only. It is not drawn to scale. 
     The functions  212   a ,  212   b ,  212   c ,  212   d ,  212   e  have several characteristics in common. Firstly, they have a maximum  601  for a first intensity value  602  within the range  514  of the intensity values of the changed pixels  408 . The maximum  601  may take the value one, meaning that the function assigns the value one to the first intensity value. The maximum  601  may be located inside the range  514  and not at one of its end points, such as at the upper end point “Imax”. This is particularly advantageous if the range  514  is close to the upper end of the intensity scale, since otherwise there is a risk that the intensities in the range  514  saturate upon being redistributed using the function  212 . For functions  212   c  and  212   e , the maximum  601  is not achieved for a single first intensity value but for intensity values within a sub-range of the range  514 . 
     Secondly, the functions  212   a ,  212   b ,  212   c ,  212   d ,  212   e  decay with increasing distance in intensity from the first intensity value  602 . For functions  212   a ,  212   b ,  212   d  the decay is strict, while for functions  212   c  and  212   e  the decay is non-strict for a sub-range of intensities. 
     Thirdly, the functions  212   a ,  212   b ,  212   c ,  212   d ,  212   e  assign lower values to intensity values outside of the range  514  than to the first intensity value  602 . Functions  212   a ,  212   b ,  212   d ,  212   e  assign lower values to intensity values outside of the range  514  than to each intensity value within the range. This is however not the case for the function  212   d.    
     The function  212  may generally be varied within the boundaries set by these common characteristics, and many more examples than those shown in  FIG. 6  may be considered. To exemplify, the function  212  may be symmetric around the first intensity value  602  (cf. functions  212   a ,  212   b ,  212   c ,  212   e ), or it may be asymmetric around the first intensity value (cf. function  212   d ). For example, the function  212  may be skewed around the first intensity value  602 . As illustrated by example function  212   d , the function  212  may have a heavier tail for higher intensity values than for lower intensity values. This will serve to give more emphasis to higher intensities than to lower intensities. This may be relevant in a situation where the images depict objects which are warmer than the background. In the opposite situation where the objects of interest are colder than the background, such as animals on a hot savannah, the tail may instead be heavier for lower intensities. The function  212  may be continuous (cf. functions  212   a ,  212   b ,  212   c ,  212   d ) or discontinuous (cf. function  212   e ). Further, the function  212  may be smooth (such as being differentiable, cf. functions  212   a ,  212   b ,  212   d ) or non-smooth (cf. function  121   e ). 
     In order to determine a function  212  which has the common characteristics described above, the redistribution function determiner  206  may use a parametric function. The parametric function may, for example, be a Gaussian function or a skewed Gaussian function. Functions  212   a  and  212   b  schematically illustrates Gaussian functions, while function  212   d  schematically illustrates a skewed Gaussian function. A parametric function is completely defined by a set of parameters. The redistribution function determiner  206  may determine the parameters of the parametric function based on the intensity values of the changed pixels  408 , such as based on various properties of the distribution of the changed pixels  408 . These properties may include one or more of a mean of the intensity values, a mode of the intensity values, a mid-point of the range of the intensity values, and the variability of the intensity values. For example, the location of the maximum of the function may be determined based on the mean of the intensity values, the mode of the intensity values, or the mid-point of the range of the intensity values. Further, the width of the function (which is inversely related to the rate of decay of the function) may be set depending on the variability of the intensity values of the changed pixels  408 , and in some cases also depending on the variability of the non-changed pixels in the current image  103   a.    
     To exemplify, a Gaussian function is of the form: 
               f   ⁡     (   x   )       =       ae     -         (     x   -   b     )     2       2   ⁢     c   2             .           
It has three parameters: a gain parameter a, a location parameter b, and a parameter c which governs the width of the curve (and 1/c hence governs the rate of decay of the curve). The gain parameter a may be set to be equal to one, although this is not a necessary requirement.
 
     The Gaussian function has its maximum for x=b. Accordingly, the location parameter b corresponds to the first intensity value  602 . The redistribution function determiner  206  may set the location parameter b to be equal to one of a mode of the intensity values of the changed pixels  408  (i.e., the intensity value where the histogram  510  has its maximum), a mean value of the intensity values of the changed pixels  408 , and a mid-point of the range  514  (i.e., Imin+(Imax−Imin)/2). 
     The width of the function, i.e., the parameter c may be set depending on the variation of the intensity values of the changed pixels  408 . Generally, the higher the variation of the intensity values, the wider the width of the function. The variation of the intensity values of the changed pixels may be determined from the standard deviation of the intensity values of the changed pixels  408 , or from the width of the range  514  of the intensity values of the changed pixels  408 . For example, the redistribution function determiner  206  may set the width parameter c as c=d(Imax−Imin), for some factor d. Referring to functions  212   a ,  212   b  of  FIG. 6 , the factor d is set to be larger for function  212   b  than for function  212   a.    
     The factor d may have a fixed value which is empirically found to be suitable for a certain application. Alternatively, the factor d may be set depending on a variability of the intensities for pixels in the current image  103   a  which are not identified as changed. For example, the factor d may be proportional to the variability of the intensity values of the non-changed pixels. The variability may again be estimated by calculating a standard deviation or by calculating the width of the range of the concerned intensity values. 
     According to step S 08  in  FIG. 3 , once the function  212  has been determined, the intensity value redistributor  208  uses the function  212  to redistribute intensity values of pixels in the current image  103   a  so as to produce an enhanced image of the sequence  105 . In other words, the function  212  is used to redistribute intensity values in both the changed pixels  408  and the non-changed pixels. The redistribution of intensity values is applied to a majority of the pixels in the current image  103   a , and preferably to all pixels in the current image  103   a.    
     In more detail, the function  212  may be used to calculate a weighting factor for the pixels in the current image  103   a . The weighting factor is calculated for a pixel by evaluating the function  212  for the intensity value in the pixel. The enhanced image of the sequence  105  is then determined by multiplying the intensity values in the pixels by their corresponding weighting factors. For example, denoting the intensity value in a pixel by I, and the function  212  by ƒ, the intensity value of a pixel in the enhanced image of the sequence  105  is calculated as I·ƒ(I). 
     Due to the properties of the function  212  described above, the function  212  will generally assign a larger weighting factor to intensity values within the range  514  than to intensity values outside of the range  514 . Specifically, a larger weighting factor will be applied to intensity values of the changed pixels corresponding to the first, moving, object  402 , than to the intensity values of the background  406 . As a result, the contrast between the first object  402  and the background  406  is increased. Notably, also the second object  404  (which, similarly to the first object  402 , is a human being) will be given a larger weighting factor than the background  406  since its intensity values typically are within the range  514 . 
     The effect of applying the function  212  is hence that the intensity values in the image are redistributed. In other words, the distribution of the intensity values is changed from a first distribution to a second distribution. This is further exemplified in  FIG. 5 a    and  FIG. 5 c   .  FIG. 5 a    illustrates a histogram  500  of the current image  103   a , while  FIG. 5 c    illustrates a histogram  520  of the enhanced image of the sequence  105 , i.e., after redistribution of the intensity values in the current image  103   a . From  FIG. 5 c   , it is seen that the intensities of the background (corresponding to a peak  521 ) is shifted towards lower intensities such that they are further separated from the intensities of the objects  402 ,  404  (corresponding to a peak  522 ). 
     In the example above, there was only one type of object in the scene, namely the human beings  402  and  404 . Depending on the application, there may be several types of objects in the scene. The different types of objects may be associated with different temperatures and may hence show up as different peaks in the histogram of intensity values. Particularly, there may be several distinct peaks in the histogram associated with the intensity values of the changed pixels. In other words, the histogram may be multi-modal. In such a situation, the different peaks in the histogram may be considered separately from each other. In more detail, a function may be determined for each peak in the histogram in the manner explained above. Each function will thus have a maximum for an intensity within a range associated with one of the peaks, and decay with increasing distance from that distance such that it assigns lower values to intensity values outside of the range than to the intensity for which the maximum is reached. The different peaks may be given different weights in relation to each other. The relation between the weights may, for example, represent a strength ratio between the peaks. The different weights may be implemented by allowing the gain parameter a described above to be different for different peaks. The determined functions may then be merged into a single function, e.g., by taking the maximum of the functions for each intensity value. It is understood that there are other options for merging the plurality of functions into a single function. This includes taking a mean value of the functions for intensity values where they overlap, taking a maximum of the functions for intensity values corresponding to the changed pixels and taking a minimum of the functions for intensity values corresponding to the non-changed pixels. The single function may then be used to redistribute the intensity values of all pixels in the image in accordance with the above. 
     It will be appreciated that a person skilled in the art can modify the above-described embodiments in many ways and still use the advantages of the invention as shown in the embodiments above. Thus, the invention should not be limited to the shown embodiments but should only be defined by the appended claims. Additionally, as the skilled person understands, the shown embodiments may be combined.