A processor implemented method for determining dynamism in a scene, wherein determining dynamism in the scene refers to detecting dynamic movement of one or more objects in the scene, the processor implemented method comprising: collecting a depth image of the scene at time instance 't' as a reference image, via one or more hardware processors, wherein a depth sensor noise associated with the collected reference image is modelled as an ergodic stochastic process; re-projecting a plurality of historical depth images onto the time instance 't', via the one or more hardware processors, wherein the plurality of historical depth images are images of the same scene taken prior to the time instance 't'; building a spatial distribution at each of a plurality of pixels of the reference image, by processing the reference image via the one or more hardware processors; building a temporal distribution at each of the plurality of pixels of the reference image, by processing the plurality of re-projected historical depth images via the one or more hardware processors, wherein the depth sensor noise is modelled as the ergodic stochastic process includes determining that the spatial distribution estimated at each reference pixel from a plurality of neighborhood pixels is statistically the same as the temporal distribution estimated from an evolution of the reference pixel over the time, and wherein the evolution of the reference pixel is a change of a pixel at the reference image, measured from the re-projected historical depth images at the same pixel; determining divergence at each of the plurality of pixels of the reference image, based on the spatial distribution and the temporal distribution, via the one or more hardware processors, wherein the step of determining the divergence at the each of the plurality of pixels of the reference image comprises processing data pertaining to the spatial distribution and the temporal distribution and determining a value of the divergence between the spatial distribution and the temporal distribution at the each of the plurality of pixels; and determining dynamism in the scene, based on the determined divergence in one or more of the plurality of the pixels, via the one or more hardware processors. collecting a depth image of the scene at time instance 't' as a reference image, via one or more hardware processors, wherein a depth sensor noise associated with the collected reference image is modelled as an ergodic stochastic process; re-projecting a plurality of historical depth images onto the time instance 't', via the one or more hardware processors, wherein the plurality of historical depth images are images of the same scene taken prior to the time instance 't'; building a spatial distribution at each of a plurality of pixels of the reference image, by processing the reference image via the one or more hardware processors; building a temporal distribution at each of the plurality of pixels of the reference image, by processing the plurality of re-projected historical depth images via the one or more hardware processors, wherein the depth sensor noise is modelled as the ergodic stochastic process includes determining that the spatial distribution estimated at each reference pixel from a plurality of neighborhood pixels is statistically the same as the temporal distribution estimated from an evolution of the reference pixel over the time, and wherein the evolution of the reference pixel is a change of a pixel at the reference image, measured from the re-projected historical depth images at the same pixel; determining divergence at each of the plurality of pixels of the reference image, based on the spatial distribution and the temporal distribution, via the one or more hardware processors, wherein the step of determining the divergence at the each of the plurality of pixels of the reference image comprises processing data pertaining to the spatial distribution and the temporal distribution and determining a value of the divergence between the spatial distribution and the temporal distribution at the each of the plurality of pixels; and determining dynamism in the scene, based on the determined divergence in one or more of the plurality of the pixels, via the one or more hardware processors.