<p>Many methods based on frame difference are developed for the detection of objects in a video. However, selecting moving pixels relevant to an object in a video and detecting the object under different environmental conditions is still a challenging task. In this work, a modified inter-frame difference (MIFD) method for detecting moving objects in a video at various conditions is proposed. The method constructs a motion feature matrix with enhanced intensity values, say motion frame. These intensity values are based on a product of a constant parameter (<InlineEquation ID="IEq1"> <InlineMediaObject> <ImageObject Color="BlackWhite" FileRef="41870_2024_2355_Article_IEq1.gif" Format="GIF" Height="17" Rendition="HTML" Resolution="72" Type="Linedraw" Width="15" /> </InlineMediaObject> <EquationSource Format="TEX">\(\beta \)</EquationSource> <EquationSource Format="MATHML"><math> <mi>β</mi> </math></EquationSource> </InlineEquation>). The values of <InlineEquation ID="IEq2"> <InlineMediaObject> <ImageObject Color="BlackWhite" FileRef="41870_2024_2355_Article_IEq2.gif" Format="GIF" Height="17" Rendition="HTML" Resolution="72" Type="Linedraw" Width="15" /> </InlineMediaObject> <EquationSource Format="TEX">\(\beta \)</EquationSource> <EquationSource Format="MATHML"><math> <mi>β</mi> </math></EquationSource> </InlineEquation> are different for different video sequences. The proposed model’s motion feature matrix enhances the relevance of pixels associated with an object. It leads to more accurate detection. Ostu’s threshold method is used in the detection process. We experimentally examine the performance of the proposed model using benchmark datasets including changedetection.net (CDNet2014 dataset). The superior performance of the proposed model compared to state-of-the-art methods is demonstrated on the CDNet2014 dataset.</p>