Pedestrian　attributes　(such　as　gender,　age,　hairstyle,　and　clothing)　can　effectively　represent　the　appearance　of　pedestrians.　These　are　high-level　semantic　features　that　are　robust　to　illumination,　deformation,　etc.　Therefore,　they　can　be　widely　used　in　person　re-identification,　video　structuring　analysis　and　other　applications.　In　this　paper,　a　pedestrian　attributes　recognition　method　for　surveillance　scenarios　using　a　multi-task　lightweight　convolutional　neural　network　is　proposed.　Firstly,　the　labels　of　the　attributes　for　each　pedestrian　image　are　integrated　into　a　label　vector.　Then,　a　multi-task　lightweight　Convolutional　Neural　Network　(CNN)　is　designed,　which　consists　of　five　convolutional　layers,　three　pooling　layers　and　two　fully　connected　layers　to　extract　the　deep　features　of　pedestrian　images.　Considering　that　the　data　distribution　of　the　datasets　is　unbalanced,　the　loss　function　is　improved　based　on　the　sigmoid　cross-entropy,　and　the　scale　factor　is　added　to　balance　the　amount　of　various　attributes　data.　Through　training　the　network,　the　mapping　relationship　model　between　the　deep　features　of　pedestrian　images　and　the　integration　label　vector　of　their　attributes　is　established,　which　can　be　used　to　predict　each　attribute　of　the　pedestrian.　The　experiments　were　conducted　on　two　public　pedestrian　attributes　datasets　in　surveillance　scenarios,　namely　PETA　and　RAP.　The　results　show　that,　compared　with　the　state-of-the-art　pedestrian　attributes　recognition　methods,　the　proposed　method　can　achieve　a　superior　accuracy　by　91.88%　on　PETA　and　87.44%　on　RAP　respectively.