For　videos　captured　by　in-car　cameras,　the　filter-based　tracking　is　a　challenging　task　due　to　complex　environments　and　mutable　object　scales.　A　scale　adaptive　tracking　filter　is　proposed　based　on　the　background　information.　Firstly,　the　relative　motion　of　each　object　is　estimated　by　extracting　features　from　gradient　histograms　between　frames.　Then,　the　object　location　on　the　next　frame　is　determined　and　utilized　to　delimit　an　image　block.　Finally,　the　object　scale　is　obtained　through　dynamic　scaling　pyramid　model　within　image　block.　The　proposed　algorithm　is　examined　by　27　in-car　videos　including　23　KITTI　videos　and　4　domestic　videos.　In　experiments,　the　proposed　algorithm　suppresses　effectively　the　interferences　of　environments　and　objects.　It　achieves　more　accurate　and　more　robust　object　tracking　than　several　popular　benchmarks　including　KCF,　DSST,　SAMF,　SATPLE.　©　2018,　Science　Press.　All　right　reserved.